Import Libraries

import pandas as pd
import numpy as np

from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.feature_selection import SequentialFeatureSelector
from scipy import stats
from sklearn.preprocessing import StandardScaler,MinMaxScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor

import seaborn as sns
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings('ignore')

pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 100)

1) Prepare data for Analysis, Treat Missing Data, Wrong Data, Outlier Data

Read the data

df = pd.read_excel("Attrition Case Study.xlsx")
df.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

df.shape

(1470, 35)

df.columns

Index(['Attrition', 'Age', 'BusinessTravel', 'DailyRate', 'Department',
       'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Attrition                 1470 non-null   int64 
 1   Age                       1470 non-null   int64 
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(27), object(8)
memory usage: 402.1+ KB

df.isna().sum()

Attrition                   0
Age                         0
BusinessTravel              0
DailyRate                   0
Department                  0
DistanceFromHome            0
Education                   0
EducationField              0
EmployeeCount               0
EmployeeNumber              0
EnvironmentSatisfaction     0
Gender                      0
HourlyRate                  0
JobInvolvement              0
JobLevel                    0
JobRole                     0
JobSatisfaction             0
MaritalStatus               0
MonthlyIncome               0
MonthlyRate                 0
NumCompaniesWorked          0
Over18                      0
OverTime                    0
PercentSalaryHike           0
PerformanceRating           0
RelationshipSatisfaction    0
StandardHours               0
StockOptionLevel            0
TotalWorkingYears           0
TrainingTimesLastYear       0
WorkLifeBalance             0
YearsAtCompany              0
YearsInCurrentRole          0
YearsSinceLastPromotion     0
YearsWithCurrManager        0
dtype: int64

new_df= df.drop(["Attrition"],axis=1)
new_df.head()

	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

new_df.shape

(1470, 34)

cat = []
con = []
for i in new_df.columns:
    if(df[i].dtypes == "object"):
        cat.append(i)
    else:
        con.append(i)

cat,con

(['BusinessTravel',
  'Department',
  'EducationField',
  'Gender',
  'JobRole',
  'MaritalStatus',
  'Over18',
  'OverTime'],
 ['Age',
  'DailyRate',
  'DistanceFromHome',
  'Education',
  'EmployeeCount',
  'EmployeeNumber',
  'EnvironmentSatisfaction',
  'HourlyRate',
  'JobInvolvement',
  'JobLevel',
  'JobSatisfaction',
  'MonthlyIncome',
  'MonthlyRate',
  'NumCompaniesWorked',
  'PercentSalaryHike',
  'PerformanceRating',
  'RelationshipSatisfaction',
  'StandardHours',
  'StockOptionLevel',
  'TotalWorkingYears',
  'TrainingTimesLastYear',
  'WorkLifeBalance',
  'YearsAtCompany',
  'YearsInCurrentRole',
  'YearsSinceLastPromotion',
  'YearsWithCurrManager'])

df_cat = new_df[cat]
df_cat

	BusinessTravel	Department	EducationField	Gender	JobRole	MaritalStatus	Over18	OverTime
0	Travel_Rarely	Sales	Life Sciences	Female	Sales Executive	Single	Y	Yes
1	Travel_Frequently	Research & Development	Life Sciences	Male	Research Scientist	Married	Y	No
2	Travel_Rarely	Research & Development	Other	Male	Laboratory Technician	Single	Y	Yes
3	Travel_Frequently	Research & Development	Life Sciences	Female	Research Scientist	Married	Y	Yes
4	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
...	...	...	...	...	...	...	...	...
1465	Travel_Frequently	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
1466	Travel_Rarely	Research & Development	Medical	Male	Healthcare Representative	Married	Y	No
1467	Travel_Rarely	Research & Development	Life Sciences	Male	Manufacturing Director	Married	Y	Yes
1468	Travel_Frequently	Sales	Medical	Male	Sales Executive	Married	Y	No
1469	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No

1470 rows × 8 columns

df_con = new_df[con]
df_con

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	1102	1	2	1	1	2	94	3	2	4	5993	19479	8	11	3	1	80	0	8	0	1	6	4	0	5
1	49	279	8	1	1	2	3	61	2	2	2	5130	24907	1	23	4	4	80	1	10	3	3	10	7	1	7
2	37	1373	2	2	1	4	4	92	2	1	3	2090	2396	6	15	3	2	80	0	7	3	3	0	0	0	0
3	33	1392	3	4	1	5	4	56	3	1	3	2909	23159	1	11	3	3	80	0	8	3	3	8	7	3	0
4	27	591	2	1	1	7	1	40	3	1	2	3468	16632	9	12	3	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	36	884	23	2	1	2061	3	41	4	2	4	2571	12290	4	17	3	3	80	1	17	3	3	5	2	0	3
1466	39	613	6	1	1	2062	4	42	2	3	1	9991	21457	4	15	3	1	80	1	9	5	3	7	7	1	7
1467	27	155	4	3	1	2064	2	87	4	2	2	6142	5174	1	20	4	2	80	1	6	0	3	6	2	0	3
1468	49	1023	2	3	1	2065	4	63	2	2	2	5390	13243	2	14	3	4	80	0	17	3	2	9	6	0	8
1469	34	628	8	3	1	2068	2	82	4	2	3	4404	10228	2	12	3	1	80	0	6	3	4	4	3	1	2

1470 rows × 26 columns

Outliers

#we check and handle outliers in continuous features only

df_con.boxplot()

<AxesSubplot:>

df_con.columns

Index(['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome',
       'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike',
       'PerformanceRating', 'RelationshipSatisfaction', 'StandardHours',
       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',
       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',
       'YearsSinceLastPromotion', 'YearsWithCurrManager'],
      dtype='object')

outlier = ['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EmployeeCount',
       'EmployeeNumber', 'EnvironmentSatisfaction', 'HourlyRate',
       'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome',
       'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike',
       'PerformanceRating', 'RelationshipSatisfaction', 'StandardHours',
       'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',
       'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',
       'YearsSinceLastPromotion', 'YearsWithCurrManager']

for i in outlier:
    sns.boxplot(df_con[i])
    plt.show()

for i in outlier:
    sns.boxplot(np.cbrt(df_con[i]))
    plt.show()

2) Create new/derived predictors (e.g. Age group) for analysis

df_con

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	1102	1	2	1	1	2	94	3	2	4	5993	19479	8	11	3	1	80	0	8	0	1	6	4	0	5
1	49	279	8	1	1	2	3	61	2	2	2	5130	24907	1	23	4	4	80	1	10	3	3	10	7	1	7
2	37	1373	2	2	1	4	4	92	2	1	3	2090	2396	6	15	3	2	80	0	7	3	3	0	0	0	0
3	33	1392	3	4	1	5	4	56	3	1	3	2909	23159	1	11	3	3	80	0	8	3	3	8	7	3	0
4	27	591	2	1	1	7	1	40	3	1	2	3468	16632	9	12	3	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	36	884	23	2	1	2061	3	41	4	2	4	2571	12290	4	17	3	3	80	1	17	3	3	5	2	0	3
1466	39	613	6	1	1	2062	4	42	2	3	1	9991	21457	4	15	3	1	80	1	9	5	3	7	7	1	7
1467	27	155	4	3	1	2064	2	87	4	2	2	6142	5174	1	20	4	2	80	1	6	0	3	6	2	0	3
1468	49	1023	2	3	1	2065	4	63	2	2	2	5390	13243	2	14	3	4	80	0	17	3	2	9	6	0	8
1469	34	628	8	3	1	2068	2	82	4	2	3	4404	10228	2	12	3	1	80	0	6	3	4	4	3	1	2

1470 rows × 26 columns

df_con['Age'].unique

<bound method Series.unique of 0       41
1       49
2       37
3       33
4       27
        ..
1465    36
1466    39
1467    27
1468    49
1469    34
Name: Age, Length: 1470, dtype: int64>

df_con['Age'].value_counts()

35    78
34    77
36    69
31    69
29    68
32    61
30    60
38    58
33    58
40    57
37    50
27    48
28    48
42    46
39    42
45    41
41    40
26    39
44    33
46    33
43    32
50    30
24    26
25    26
47    24
49    24
55    22
53    19
48    19
51    19
52    18
54    18
22    16
56    14
23    14
58    14
21    13
20    11
59    10
19     9
18     8
60     5
57     4
Name: Age, dtype: int64

Q = []
for i in df_con.Age:
    if(i<=18):
        Q.append("Child")
    elif(i>18 and i<=40):
        Q.append("Adult")
    else:
        Q.append("Old")

df_cat['Age Group']=Q

df_cat

	BusinessTravel	Department	EducationField	Gender	JobRole	MaritalStatus	Over18	OverTime	Age Group
0	Travel_Rarely	Sales	Life Sciences	Female	Sales Executive	Single	Y	Yes	Old
1	Travel_Frequently	Research & Development	Life Sciences	Male	Research Scientist	Married	Y	No	Old
2	Travel_Rarely	Research & Development	Other	Male	Laboratory Technician	Single	Y	Yes	Adult
3	Travel_Frequently	Research & Development	Life Sciences	Female	Research Scientist	Married	Y	Yes	Adult
4	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No	Adult
...	...	...	...	...	...	...	...	...	...
1465	Travel_Frequently	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No	Adult
1466	Travel_Rarely	Research & Development	Medical	Male	Healthcare Representative	Married	Y	No	Adult
1467	Travel_Rarely	Research & Development	Life Sciences	Male	Manufacturing Director	Married	Y	Yes	Adult
1468	Travel_Frequently	Sales	Medical	Male	Sales Executive	Married	Y	No	Old
1469	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No	Adult

1470 rows × 9 columns

OHE of categorical columns

df_cat = pd.get_dummies(df_cat)
df_cat

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0

1470 rows × 32 columns

Standardization of con columns

ss = StandardScaler()
X1 = ss.fit_transform(df_con)
X1

array([[ 0.4463504 ,  0.74252653, -1.01090934, ..., -0.0632959 ,
        -0.67914568,  0.24583399],
       [ 1.32236521, -1.2977746 , -0.14714972, ...,  0.76499762,
        -0.36871529,  0.80654148],
       [ 0.008343  ,  1.41436324, -0.88751511, ..., -1.16768726,
        -0.67914568, -1.15593471],
       ...,
       [-1.08667552, -1.60518328, -0.64072665, ..., -0.61549158,
        -0.67914568, -0.31487349],
       [ 1.32236521,  0.54667746, -0.88751511, ...,  0.48889978,
        -0.67914568,  1.08689522],
       [-0.32016256, -0.43256792, -0.14714972, ..., -0.33939374,
        -0.36871529, -0.59522723]])

df1 = pd.DataFrame(X1,columns=df_con.columns)
df1

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
2	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
3	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
4	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873
1466	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541
1467	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873
1468	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895
1469	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227

1470 rows × 26 columns

df_y = df[['Attrition']]

final_df = pd.concat([df_cat,df1,df_y],axis=1)
final_df

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager	Attrition
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834	1
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541	0
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935	1
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935	0
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873	0
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541	0
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873	0
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895	0
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227	0

1470 rows × 59 columns

3) Explore the Data using Exploratory Data Analysis - For Y and all Xs

final_df.skew()

BusinessTravel_Non-Travel             2.632066
BusinessTravel_Travel_Frequently      1.595067
BusinessTravel_Travel_Rarely         -0.923992
Department_Human Resources            4.518824
Department_Research & Development    -0.646936
Department_Sales                      0.856158
EducationField_Human Resources        7.181112
EducationField_Life Sciences          0.356919
EducationField_Marketing              2.525783
EducationField_Medical                0.794118
EducationField_Other                  3.875119
EducationField_Technical Degree       2.872604
Gender_Female                         0.408665
Gender_Male                          -0.408665
JobRole_Healthcare Representative     2.887251
JobRole_Human Resources               5.035637
JobRole_Laboratory Technician         1.701604
JobRole_Manager                       3.392611
JobRole_Manufacturing Director        2.694844
JobRole_Research Director             3.932443
JobRole_Research Scientist            1.512214
JobRole_Sales Executive               1.340834
JobRole_Sales Representative          3.847192
MaritalStatus_Divorced                1.336093
MaritalStatus_Married                 0.169484
MaritalStatus_Single                  0.773874
Over18_Y                              0.000000
OverTime_No                          -0.964489
OverTime_Yes                          0.964489
Age Group_Adult                      -0.763830
Age Group_Child                      13.458270
Age Group_Old                         0.790729
Age                                   0.413286
DailyRate                            -0.003519
DistanceFromHome                      0.958118
Education                            -0.289681
EmployeeCount                         0.000000
EmployeeNumber                        0.016574
EnvironmentSatisfaction              -0.321654
HourlyRate                           -0.032311
JobInvolvement                       -0.498419
JobLevel                              1.025401
JobSatisfaction                      -0.329672
MonthlyIncome                         1.369817
MonthlyRate                           0.018578
NumCompaniesWorked                    1.026471
PercentSalaryHike                     0.821128
PerformanceRating                     1.921883
RelationshipSatisfaction             -0.302828
StandardHours                         0.000000
StockOptionLevel                      0.968980
TotalWorkingYears                     1.117172
TrainingTimesLastYear                 0.553124
WorkLifeBalance                      -0.552480
YearsAtCompany                        1.764529
YearsInCurrentRole                    0.917363
YearsSinceLastPromotion               1.984290
YearsWithCurrManager                  0.833451
Attrition                             1.844366
dtype: float64

final_df.corr()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager	Attrition
BusinessTravel_Non-Travel	1.000000	-0.162435	-0.526850	-0.004755	-0.005013	0.007283	0.020835	0.005311	-0.030567	0.012828	-0.013389	0.004171	-0.050461	0.050461	0.012878	-0.015890	0.009270	0.014078	-0.013536	-0.021431	-0.010116	0.031022	-0.033780	0.057455	-0.043635	-0.004622	NaN	0.037163	-0.037163	-0.008345	0.097251	-0.007002	-0.011215	0.012096	0.023605	0.004524	NaN	0.022272	0.003568	-0.016994	-0.045779	-0.007295	0.019802	-0.017261	0.015279	0.002718	0.036591	0.018310	0.021132	NaN	0.028807	-0.029742	-0.020746	0.005780	0.007623	0.011549	0.020815	0.016716	-0.074457
BusinessTravel_Travel_Frequently	-0.162435	1.000000	-0.753092	-0.007485	0.003340	-0.000160	0.011818	0.031128	-0.016586	-0.005367	-0.011004	-0.023569	0.022015	-0.022015	0.008029	0.001896	0.010023	-0.042583	0.009783	-0.023579	-0.004461	-0.010175	0.055469	0.005779	-0.030785	0.027734	NaN	-0.029392	0.029392	0.022826	0.011646	-0.024772	-0.024743	-0.011776	0.005081	-0.008292	NaN	-0.007980	-0.012624	-0.018819	0.004424	-0.021557	0.027117	-0.031658	0.000344	-0.039718	-0.006675	0.016463	0.028500	NaN	-0.016142	-0.012177	0.006193	0.010199	0.012991	0.001680	0.023216	0.012628	0.115143
BusinessTravel_Travel_Rarely	-0.526850	-0.753092	1.000000	0.009618	0.000465	-0.004718	-0.024073	-0.030355	0.034668	-0.003930	0.018406	0.017521	0.014682	-0.014682	-0.015503	0.008962	-0.014815	0.027294	0.000598	0.034600	0.010588	-0.011920	-0.025257	-0.043287	0.055613	-0.020808	NaN	0.000539	-0.000539	-0.014098	-0.074876	0.026008	0.028791	0.002078	-0.020116	0.004126	NaN	-0.007976	0.008496	0.027541	0.026714	0.023433	-0.036562	0.038779	-0.010484	0.032401	-0.018649	-0.026390	-0.038640	NaN	-0.005303	0.030320	0.008498	-0.012640	-0.016274	-0.009147	-0.033877	-0.022023	-0.049538
Department_Human Resources	-0.004755	-0.007485	0.009618	1.000000	-0.290754	-0.139650	0.646436	-0.068040	-0.073692	-0.049761	-0.007527	-0.019469	-0.035652	0.035652	-0.066186	0.904983	-0.097859	0.087615	-0.070000	-0.050765	-0.105352	-0.112959	-0.051764	0.016037	0.034767	-0.051443	NaN	0.006178	-0.006178	-0.026808	-0.015653	0.029406	0.020523	-0.026726	-0.012901	0.011435	NaN	0.063431	-0.007597	-0.016551	0.004789	-0.006157	-0.024068	0.006815	-0.024390	0.020618	-0.025888	-0.006385	0.034583	NaN	-0.004000	0.007508	-0.040022	0.047763	0.007944	-0.040287	-0.026931	-0.027079	0.016832
Department_Research & Development	-0.005013	0.003340	0.000465	-0.290754	1.000000	-0.906818	-0.187954	0.127321	-0.478520	0.183548	0.064751	0.038541	-0.015760	0.015760	0.227637	-0.263128	0.336570	-0.071356	0.240754	0.174596	0.362340	-0.733497	-0.336127	0.035158	-0.019997	-0.009990	NaN	0.003036	-0.003036	-0.011570	-0.004469	0.012330	0.017883	0.014871	-0.008117	-0.018604	NaN	-0.041923	0.027976	0.018686	0.023187	-0.107830	-0.002798	-0.064720	-0.005453	0.022237	0.030735	0.032720	-0.004587	NaN	0.016927	0.011087	-0.006819	-0.069922	-0.032181	-0.028151	-0.021497	-0.014963	-0.085293
Department_Sales	0.007283	-0.000160	-0.004718	-0.139650	-0.906818	1.000000	-0.090275	-0.101791	0.527691	-0.168034	-0.063695	-0.031309	0.032017	-0.032017	-0.206425	-0.126381	-0.305208	0.035248	-0.218320	-0.158327	-0.328576	0.808869	0.370667	-0.043451	0.005378	0.033002	NaN	-0.005864	0.005864	0.023785	0.011521	-0.025715	-0.027549	-0.003616	0.014085	0.014215	NaN	0.015441	-0.025606	-0.012047	-0.026107	0.114307	0.013499	0.063978	0.016388	-0.032097	-0.020403	-0.031050	-0.010489	NaN	-0.015755	-0.014781	0.024688	0.051320	0.029805	0.046883	0.034112	0.027415	0.080855
EducationField_Human Resources	0.020835	0.011818	-0.024073	0.646436	-0.187954	-0.090275	1.000000	-0.114559	-0.047637	-0.092899	-0.033248	-0.042964	-0.028956	0.028956	-0.042785	0.549751	-0.063260	0.082271	-0.045251	-0.032816	-0.068103	-0.073020	-0.033462	0.012107	0.057339	-0.072051	NaN	-0.004040	0.004040	0.007459	-0.010119	-0.005892	0.001696	-0.043144	-0.002624	0.026479	NaN	0.035345	-0.006898	-0.033670	0.002079	0.010409	-0.021467	0.021456	0.009567	0.031007	-0.016142	-0.016167	0.041105	NaN	0.021206	0.005505	-0.037664	-0.003967	-0.005146	-0.025443	-0.023700	-0.028868	0.036466
EducationField_Life Sciences	0.005311	0.031128	-0.030355	-0.068040	0.127321	-0.101791	-0.114559	1.000000	-0.291660	-0.568774	-0.203560	-0.263050	-0.006770	0.006770	0.029084	-0.063119	0.044359	-0.011143	0.052023	0.018401	0.043729	-0.091122	-0.043208	-0.002672	-0.017866	0.021469	NaN	0.013787	-0.013787	-0.002982	-0.005597	0.003881	0.016824	0.004028	-0.024499	0.013184	NaN	-0.000609	-0.024526	0.038759	0.003228	-0.008431	0.052004	-0.007054	0.025545	-0.006131	0.010209	0.010853	-0.019973	NaN	-0.017993	-0.003630	-0.039018	-0.039728	-0.002019	0.018343	-0.002480	0.003636	-0.032703
EducationField_Marketing	-0.030567	-0.016586	0.034668	-0.073692	-0.478520	0.527691	-0.047637	-0.291660	1.000000	-0.236514	-0.084647	-0.109385	0.024143	-0.024143	-0.108929	-0.066690	-0.161055	0.025577	-0.115206	-0.083548	-0.173387	0.457308	0.133065	-0.007212	0.018491	-0.013323	NaN	-0.014607	0.014607	-0.041441	0.004010	0.040995	0.038162	-0.064449	0.039294	0.072405	NaN	-0.014487	0.000479	0.004452	-0.018657	0.092698	-0.023528	0.062576	-0.011559	-0.018611	-0.027726	-0.020918	-0.006580	NaN	0.022560	0.025779	-0.029046	0.018500	0.031365	0.025126	0.006219	0.033418	0.055781
EducationField_Medical	0.012828	-0.005367	-0.003930	-0.049761	0.183548	-0.168034	-0.092899	-0.568774	-0.236514	1.000000	-0.165072	-0.213314	0.013146	-0.013146	0.034165	-0.042895	0.066262	-0.001128	0.035496	0.062898	0.039735	-0.133532	-0.051990	0.013316	-0.007139	-0.004249	NaN	-0.002246	0.002246	0.004075	0.029342	-0.008736	-0.006354	0.034202	0.013486	-0.072335	NaN	-0.008689	-0.021299	-0.020418	0.017103	-0.014114	-0.022645	0.001025	-0.001723	0.024826	0.029116	0.014868	0.030494	NaN	0.033750	0.024890	0.070542	0.001641	0.010805	-0.026418	0.022665	-0.011544	-0.046999
EducationField_Other	-0.013389	-0.011004	0.018406	-0.007527	0.064751	-0.063695	-0.033248	-0.203560	-0.084647	-0.165072	1.000000	-0.076343	-0.022992	0.022992	0.017609	0.001594	0.058759	-0.008046	-0.010820	-0.006044	0.005286	-0.036995	-0.033774	0.005411	-0.009171	0.004972	NaN	-0.024970	0.024970	0.027823	-0.017980	-0.025105	-0.041466	-0.003893	-0.007969	0.038043	NaN	0.010432	0.064602	-0.042163	-0.011895	-0.016724	0.003380	-0.022279	-0.035606	-0.012870	0.019297	0.011449	-0.020305	NaN	-0.042100	-0.028935	-0.008151	0.031812	-0.030331	-0.017021	-0.039931	-0.011714	-0.017898
EducationField_Technical Degree	0.004171	-0.023569	0.017521	-0.019469	0.038541	-0.031309	-0.042964	-0.263050	-0.109385	-0.213314	-0.076343	1.000000	-0.003886	0.003886	0.018681	-0.008623	-0.026589	-0.038946	0.007817	-0.022905	0.076218	-0.058843	0.057185	-0.019243	0.002710	0.014265	NaN	0.017723	-0.017723	0.017692	-0.023234	-0.014097	-0.027604	0.030869	-0.014802	-0.026742	NaN	0.005938	0.027713	0.011283	-0.004519	-0.054707	-0.019795	-0.049695	-0.004535	-0.013819	-0.042701	-0.021729	-0.011044	NaN	-0.024560	-0.041577	0.008289	0.021962	-0.021399	0.009683	0.003853	-0.000836	0.069355
Gender_Female	-0.050461	0.022015	0.014682	-0.035652	-0.015760	0.032017	-0.028956	-0.006770	0.024143	0.013146	-0.022992	-0.003886	1.000000	-1.000000	-0.006823	-0.036082	-0.067793	0.033880	0.065197	0.006121	-0.009745	0.005348	0.028877	-0.046076	0.007804	0.032752	NaN	-0.041924	0.041924	-0.023185	0.015100	0.020902	0.036311	0.011716	0.001851	0.016547	NaN	-0.022556	-0.000508	0.000478	-0.017960	0.039403	-0.033252	0.031858	0.041482	0.039147	-0.002733	0.013859	-0.022868	NaN	-0.012716	0.046881	0.038787	0.002753	0.029747	0.041483	0.026985	0.030599	-0.029453
Gender_Male	0.050461	-0.022015	-0.014682	0.035652	0.015760	-0.032017	0.028956	0.006770	-0.024143	-0.013146	0.022992	0.003886	-1.000000	1.000000	0.006823	0.036082	0.067793	-0.033880	-0.065197	-0.006121	0.009745	-0.005348	-0.028877	0.046076	-0.007804	-0.032752	NaN	0.041924	-0.041924	0.023185	-0.015100	-0.020902	-0.036311	-0.011716	-0.001851	-0.016547	NaN	0.022556	0.000508	-0.000478	0.017960	-0.039403	0.033252	-0.031858	-0.041482	-0.039147	0.002733	-0.013859	0.022868	NaN	0.012716	-0.046881	-0.038787	-0.002753	-0.029747	-0.041483	-0.026985	-0.030599	0.029453
JobRole_Healthcare Representative	0.012878	0.008029	-0.015503	-0.066186	0.227637	-0.206425	-0.042785	0.029084	-0.108929	0.034165	0.017609	0.018681	-0.006823	0.006823	1.000000	-0.059898	-0.144652	-0.085409	-0.103472	-0.075038	-0.155727	-0.166971	-0.076515	0.027897	0.004913	-0.030126	NaN	0.000382	-0.000382	-0.050336	-0.023138	0.054225	0.098825	0.040141	0.022916	0.024270	NaN	0.025945	0.014090	0.014599	0.001272	0.115704	0.016367	0.068177	0.003829	0.026955	0.020591	-0.000928	-0.005090	NaN	0.014021	0.112159	-0.012432	-0.026101	0.069758	0.054695	0.075902	0.039407	-0.078696
JobRole_Human Resources	-0.015890	0.001896	0.008962	0.904983	-0.263128	-0.126381	0.549751	-0.063119	-0.066690	-0.042895	0.001594	-0.008623	-0.036082	0.036082	-0.059898	1.000000	-0.088561	-0.052290	-0.063349	-0.045941	-0.095342	-0.102226	-0.046845	0.021541	0.030995	-0.052320	NaN	0.014026	-0.014026	0.021537	-0.014166	-0.019393	-0.029856	-0.021156	-0.024089	-0.005295	NaN	0.067287	-0.022014	-0.016189	-0.004952	-0.100922	-0.029681	-0.092250	-0.027470	0.020578	-0.021032	-0.010154	0.044169	NaN	-0.009864	-0.076482	-0.035902	0.043887	-0.052569	-0.057876	-0.054603	-0.051006	0.036215
JobRole_Laboratory Technician	0.009270	0.010023	-0.014815	-0.097859	0.336570	-0.305208	-0.063260	0.044359	-0.161055	0.066262	0.058759	-0.026589	-0.067793	0.067793	-0.144652	-0.088561	1.000000	-0.126280	-0.152987	-0.110947	-0.230248	-0.246873	-0.113130	-0.011224	-0.009233	0.019873	NaN	0.044774	-0.044774	0.100670	0.038601	-0.107235	-0.143176	-0.006728	0.012369	-0.063566	NaN	-0.019722	-0.001533	0.018028	-0.022724	-0.344608	-0.015710	-0.320906	-0.016056	-0.021121	-0.020628	0.010796	-0.010691	NaN	0.013386	-0.215426	0.053998	-0.028209	-0.150181	-0.131322	-0.110099	-0.107072	0.098290
JobRole_Manager	0.014078	-0.042583	0.027294	0.087615	-0.071356	0.035248	0.082271	-0.011143	0.025577	-0.001128	-0.008046	-0.038946	0.033880	-0.033880	-0.085409	-0.052290	-0.126280	1.000000	-0.090330	-0.065508	-0.135949	-0.145765	-0.066797	0.001997	0.049982	-0.055176	NaN	0.011086	-0.011086	-0.293287	-0.020199	0.297815	0.294248	-0.013224	-0.039190	0.028453	NaN	-0.035058	0.010730	0.012659	0.017112	0.552744	-0.005620	0.619573	0.031717	0.042125	-0.005394	0.032050	0.025638	NaN	-0.015637	0.465837	0.003052	0.005137	0.330965	0.167499	0.224255	0.164695	-0.083316
JobRole_Manufacturing Director	-0.013536	0.009783	0.000598	-0.070000	0.240754	-0.218320	-0.045251	0.052023	-0.115206	0.035496	-0.010820	0.007817	0.065197	-0.065197	-0.103472	-0.063349	-0.152987	-0.090330	1.000000	-0.079362	-0.164700	-0.176592	-0.080924	0.020543	0.002819	-0.021331	NaN	0.010302	-0.010302	0.003206	-0.024471	0.000651	0.049726	-0.005302	0.011848	-0.005290	NaN	-0.014350	0.059178	-0.014394	-0.021939	0.114896	-0.013747	0.055684	0.007711	0.009580	0.034682	0.029775	0.003640	NaN	0.007735	0.064077	-0.013987	0.002011	0.031968	0.067877	-0.007241	0.076207	-0.082994
JobRole_Research Director	-0.021431	-0.023579	0.034600	-0.050765	0.174596	-0.158327	-0.032816	0.018401	-0.083548	0.062898	-0.006044	-0.022905	0.006121	-0.006121	-0.075038	-0.045941	-0.110947	-0.065508	-0.079362	1.000000	-0.119442	-0.128066	-0.058687	0.037524	0.008271	-0.042299	NaN	-0.002400	0.002400	-0.174980	-0.017746	0.178583	0.185891	-0.000021	-0.022351	0.049694	NaN	-0.013983	-0.048689	-0.025128	0.015200	0.414319	-0.006217	0.485818	0.025875	0.097925	-0.017017	-0.035744	-0.005492	NaN	0.015807	0.312148	-0.004527	0.034403	0.153918	0.136332	0.074455	0.131279	-0.088870
JobRole_Research Scientist	-0.010116	-0.004461	0.010588	-0.105352	0.362340	-0.328576	-0.068103	0.043729	-0.173387	0.039735	0.005286	0.076218	-0.009745	0.009745	-0.155727	-0.095342	-0.230248	-0.135949	-0.164700	-0.119442	1.000000	-0.265775	-0.121792	-0.012115	-0.039987	0.053522	NaN	-0.054378	0.054378	0.109337	0.009523	-0.111340	-0.146518	-0.002624	-0.010986	0.000709	NaN	-0.017686	0.001940	0.020034	0.047604	-0.387788	0.020503	-0.345180	-0.027008	-0.043981	0.032537	0.019416	-0.003116	NaN	-0.011635	-0.228119	-0.052126	-0.058613	-0.154062	-0.131314	-0.105237	-0.127608	-0.000360
JobRole_Sales Executive	0.031022	-0.010175	-0.011920	-0.112959	-0.733497	0.808869	-0.073020	-0.091122	0.457308	-0.133532	-0.036995	-0.058843	0.005348	-0.005348	-0.166971	-0.102226	-0.246873	-0.145765	-0.176592	-0.128066	-0.265775	1.000000	-0.130586	-0.013853	0.005751	0.006210	NaN	-0.006341	0.006341	0.048711	-0.039488	-0.042685	-0.002001	-0.000513	0.030761	0.053398	NaN	0.023263	-0.024421	-0.011886	-0.011413	0.127490	0.012604	0.047792	0.011854	0.005913	-0.046683	-0.041401	-0.004836	NaN	0.015756	-0.012241	0.013241	0.032092	0.042602	0.092349	0.049202	0.083028	0.019774
JobRole_Sales Representative	-0.033780	0.055469	-0.025257	-0.051764	-0.336127	0.370667	-0.033462	-0.043208	0.133065	-0.051990	-0.033774	0.057185	0.028877	-0.028877	-0.076515	-0.046845	-0.113130	-0.066797	-0.080924	-0.058687	-0.121792	-0.130586	1.000000	-0.052890	-0.023659	0.072439	NaN	-0.003347	0.003347	0.092786	0.102087	-0.109357	-0.175785	0.005375	-0.015994	-0.091465	NaN	0.006255	0.002949	-0.018703	-0.027282	-0.216559	0.001413	-0.201514	-0.001200	-0.104494	0.031102	-0.006214	-0.024859	NaN	-0.048067	-0.207726	0.040377	0.045148	-0.163464	-0.149751	-0.085622	-0.168743	0.157234
MaritalStatus_Divorced	0.057455	0.005779	-0.043287	0.016037	0.035158	-0.043451	0.012107	-0.002672	-0.007212	0.013316	0.005411	-0.019243	-0.046076	0.046076	0.027897	0.021541	-0.011224	0.001997	0.020543	0.037524	-0.012115	-0.013853	-0.052890	1.000000	-0.491506	-0.366691	NaN	-0.023462	0.023462	-0.016742	-0.039566	0.023078	0.033120	0.037080	-0.005440	-0.002439	NaN	-0.025149	0.016439	-0.006150	0.016815	0.037087	-0.015197	0.032203	-0.000227	0.040824	-0.023478	-0.010310	0.006199	NaN	0.446285	0.036291	0.008405	-0.009080	0.025728	0.018532	-0.005279	0.014095	-0.087716
MaritalStatus_Married	-0.043635	-0.030785	0.055613	0.034767	-0.019997	0.005378	0.057339	-0.017866	0.018491	-0.007139	-0.009171	0.002710	0.007804	-0.007804	0.004913	0.030995	-0.009233	0.049982	0.002819	0.008271	-0.039987	0.005751	-0.023659	-0.491506	1.000000	-0.629981	NaN	0.013502	-0.013502	-0.036388	-0.067975	0.047307	0.083919	0.040035	0.030232	-0.001865	NaN	0.053933	-0.022180	0.036432	0.028324	0.050547	-0.010315	0.056767	-0.034689	-0.016142	0.020895	0.009585	-0.043382	NaN	0.225574	0.053512	-0.029602	-0.006388	0.044925	0.065488	0.054102	0.032972	-0.090984
MaritalStatus_Single	-0.004622	0.027734	-0.020808	-0.051443	-0.009990	0.033002	-0.072051	0.021469	-0.013323	-0.004249	0.004972	0.014265	0.032752	-0.032752	-0.030126	-0.052320	0.019873	-0.055176	-0.021331	-0.042299	0.053522	0.006210	0.072439	-0.366691	-0.629981	1.000000	NaN	0.006498	-0.006498	0.053803	0.107900	-0.071118	-0.119185	-0.075835	-0.027445	0.004168	NaN	-0.035189	0.009035	-0.033436	-0.045253	-0.087072	0.024571	-0.089361	0.037260	-0.019161	-0.001386	-0.001045	0.040817	NaN	-0.638957	-0.089529	0.024129	0.014921	-0.070935	-0.086486	-0.053090	-0.047793	0.175419
Over18_Y	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
OverTime_No	0.037163	-0.029392	0.000539	0.006178	0.003036	-0.005864	-0.004040	0.013787	-0.014607	-0.002246	-0.024970	0.017723	-0.041924	0.041924	0.000382	0.014026	0.044774	0.011086	0.010302	-0.002400	-0.054378	-0.006341	-0.003347	-0.023462	0.013502	0.006498	NaN	1.000000	-1.000000	0.039259	0.005418	-0.040295	-0.028062	-0.009135	-0.025514	0.020322	NaN	0.024037	-0.070132	0.007782	0.003507	-0.000544	-0.024539	-0.006089	-0.021431	0.020786	0.005433	-0.004369	-0.048493	NaN	0.000449	-0.012754	0.079113	0.027092	0.011687	0.029758	0.012239	0.041586	-0.246118
OverTime_Yes	-0.037163	0.029392	-0.000539	-0.006178	-0.003036	0.005864	0.004040	-0.013787	0.014607	0.002246	0.024970	-0.017723	0.041924	-0.041924	-0.000382	-0.014026	-0.044774	-0.011086	-0.010302	0.002400	0.054378	0.006341	0.003347	0.023462	-0.013502	-0.006498	NaN	-1.000000	1.000000	-0.039259	-0.005418	0.040295	0.028062	0.009135	0.025514	-0.020322	NaN	-0.024037	0.070132	-0.007782	-0.003507	0.000544	0.024539	0.006089	0.021431	-0.020786	-0.005433	0.004369	0.048493	NaN	-0.000449	0.012754	-0.079113	-0.027092	-0.011687	-0.029758	-0.012239	-0.041586	0.246118
Age Group_Adult	-0.008345	0.022826	-0.014098	-0.026808	-0.011570	0.023785	0.007459	-0.002982	-0.041441	0.004075	0.027823	0.017692	-0.023185	0.023185	-0.050336	0.021537	0.100670	-0.293287	0.003206	-0.174980	0.109337	0.048711	0.092786	-0.016742	-0.036388	0.053803	NaN	0.039259	-0.039259	1.000000	-0.107396	-0.987553	-0.791869	-0.004701	-0.005739	-0.102673	NaN	0.017838	-0.002137	-0.019811	-0.036322	-0.423418	0.007416	-0.423949	-0.018712	-0.226912	0.001236	0.002906	-0.052690	NaN	0.011119	-0.543259	0.002348	0.000122	-0.226865	-0.125668	-0.147455	-0.113396	0.080224
Age Group_Child	0.097251	0.011646	-0.074876	-0.015653	-0.004469	0.011521	-0.010119	-0.005597	0.004010	0.029342	-0.017980	-0.023234	0.015100	-0.015100	-0.023138	-0.014166	0.038601	-0.020199	-0.024471	-0.017746	0.009523	-0.039488	0.102087	-0.039566	-0.067975	0.107900	NaN	0.005418	-0.005418	-0.107396	1.000000	-0.050317	-0.153286	-0.010061	-0.028000	-0.029835	NaN	0.003535	0.001911	0.008131	0.015086	-0.071124	0.034986	-0.078345	0.001694	-0.050157	-0.032036	-0.031529	0.028249	NaN	-0.068944	-0.107273	-0.017179	0.025010	-0.084647	-0.086377	-0.050238	-0.085508	0.068147
Age Group_Old	-0.007002	-0.024772	0.026008	0.029406	0.012330	-0.025715	-0.005892	0.003881	0.040995	-0.008736	-0.025105	-0.014097	0.020902	-0.020902	0.054225	-0.019393	-0.107235	0.297815	0.000651	0.178583	-0.111340	-0.042685	-0.109357	0.023078	0.047307	-0.071118	NaN	-0.040295	0.040295	-0.987553	-0.050317	1.000000	0.819717	0.006314	0.010195	0.107859	NaN	-0.018479	0.001844	0.018615	0.034100	0.436593	-0.012984	0.438270	0.018529	0.235877	0.003827	0.002069	0.048460	NaN	-0.000263	0.562698	0.000359	-0.004079	0.241287	0.139904	0.156073	0.127439	-0.091370
Age	-0.011215	-0.024743	0.028791	0.020523	0.017883	-0.027549	0.001696	0.016824	0.038162	-0.006354	-0.041466	-0.027604	0.036311	-0.036311	0.098825	-0.029856	-0.143176	0.294248	0.049726	0.185891	-0.146518	-0.002001	-0.175785	0.033120	0.083919	-0.119185	NaN	-0.028062	0.028062	-0.791869	-0.153286	0.819717	1.000000	0.010661	-0.001686	0.208034	NaN	-0.010145	0.010146	0.024287	0.029820	0.509604	-0.004892	0.497855	0.028051	0.299635	0.003634	0.001904	0.053535	NaN	0.037510	0.680381	-0.019621	-0.021490	0.311309	0.212901	0.216513	0.202089	-0.159205
DailyRate	0.012096	-0.011776	0.002078	-0.026726	0.014871	-0.003616	-0.043144	0.004028	-0.064449	0.034202	-0.003893	0.030869	0.011716	-0.011716	0.040141	-0.021156	-0.006728	-0.013224	-0.005302	-0.000021	-0.002624	-0.000513	0.005375	0.037080	0.040035	-0.075835	NaN	-0.009135	0.009135	-0.004701	-0.010061	0.006314	0.010661	1.000000	-0.004985	-0.016806	NaN	-0.050990	0.018355	0.023381	0.046135	0.002966	0.030571	0.007707	-0.032182	0.038153	0.022704	0.000473	0.007846	NaN	0.042143	0.014515	0.002453	-0.037848	-0.034055	0.009932	-0.033229	-0.026363	-0.056652
DistanceFromHome	0.023605	0.005081	-0.020116	-0.012901	-0.008117	0.014085	-0.002624	-0.024499	0.039294	0.013486	-0.007969	-0.014802	0.001851	-0.001851	0.022916	-0.024089	0.012369	-0.039190	0.011848	-0.022351	-0.010986	0.030761	-0.015994	-0.005440	0.030232	-0.027445	NaN	-0.025514	0.025514	-0.005739	-0.028000	0.010195	-0.001686	-0.004985	1.000000	0.021042	NaN	0.032916	-0.016075	0.031131	0.008783	0.005303	-0.003669	-0.017014	0.027473	-0.029251	0.040235	0.027110	0.006557	NaN	0.044872	0.004628	-0.036942	-0.026556	0.009508	0.018845	0.010029	0.014406	0.077924
Education	0.004524	-0.008292	0.004126	0.011435	-0.018604	0.014215	0.026479	0.013184	0.072405	-0.072335	0.038043	-0.026742	0.016547	-0.016547	0.024270	-0.005295	-0.063566	0.028453	-0.005290	0.049694	0.000709	0.053398	-0.091465	-0.002439	-0.001865	0.004168	NaN	0.020322	-0.020322	-0.102673	-0.029835	0.107859	0.208034	-0.016806	0.021042	1.000000	NaN	0.042070	-0.027128	0.016775	0.042438	0.101589	-0.011296	0.094961	-0.026084	0.126317	-0.011111	-0.024539	-0.009118	NaN	0.018422	0.148280	-0.025100	0.009819	0.069114	0.060236	0.054254	0.069065	-0.031373
EmployeeCount	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
EmployeeNumber	0.022272	-0.007980	-0.007976	0.063431	-0.041923	0.015441	0.035345	-0.000609	-0.014487	-0.008689	0.010432	0.005938	-0.022556	0.022556	0.025945	0.067287	-0.019722	-0.035058	-0.014350	-0.013983	-0.017686	0.023263	0.006255	-0.025149	0.053933	-0.035189	NaN	0.024037	-0.024037	0.017838	0.003535	-0.018479	-0.010145	-0.050990	0.032916	0.042070	NaN	1.000000	0.017621	0.035179	-0.006888	-0.018519	-0.046247	-0.014829	0.012648	-0.001251	-0.012944	-0.020359	-0.069861	NaN	0.062227	-0.014365	0.023603	0.010309	-0.011240	-0.008416	-0.009019	-0.009197	-0.010577
EnvironmentSatisfaction	0.003568	-0.012624	0.008496	-0.007597	0.027976	-0.025606	-0.006898	-0.024526	0.000479	-0.021299	0.064602	0.027713	-0.000508	0.000508	0.014090	-0.022014	-0.001533	0.010730	0.059178	-0.048689	0.001940	-0.024421	0.002949	0.016439	-0.022180	0.009035	NaN	-0.070132	0.070132	-0.002137	0.001911	0.001844	0.010146	0.018355	-0.016075	-0.027128	NaN	0.017621	1.000000	-0.049857	-0.008278	0.001212	-0.006784	-0.006259	0.037600	0.012594	-0.031701	-0.029548	0.007665	NaN	0.003432	-0.002693	-0.019359	0.027627	0.001458	0.018007	0.016194	-0.004999	-0.103369
HourlyRate	-0.016994	-0.018819	0.027541	-0.016551	0.018686	-0.012047	-0.033670	0.038759	0.004452	-0.020418	-0.042163	0.011283	0.000478	-0.000478	0.014599	-0.016189	0.018028	0.012659	-0.014394	-0.025128	0.020034	-0.011886	-0.018703	-0.006150	0.036432	-0.033436	NaN	0.007782	-0.007782	-0.019811	0.008131	0.018615	0.024287	0.023381	0.031131	0.016775	NaN	0.035179	-0.049857	1.000000	0.042861	-0.027853	-0.071335	-0.015794	-0.015297	0.022157	-0.009062	-0.002172	0.001330	NaN	0.050263	-0.002334	-0.008548	-0.004607	-0.019582	-0.024106	-0.026716	-0.020123	-0.006846
JobInvolvement	-0.045779	0.004424	0.026714	0.004789	0.023187	-0.026107	0.002079	0.003228	-0.018657	0.017103	-0.011895	-0.004519	-0.017960	0.017960	0.001272	-0.004952	-0.022724	0.017112	-0.021939	0.015200	0.047604	-0.011413	-0.027282	0.016815	0.028324	-0.045253	NaN	0.003507	-0.003507	-0.036322	0.015086	0.034100	0.029820	0.046135	0.008783	0.042438	NaN	-0.006888	-0.008278	0.042861	1.000000	-0.012630	-0.021476	-0.015271	-0.016322	0.015012	-0.017205	-0.029071	0.034297	NaN	0.021523	-0.005533	-0.015338	-0.014617	-0.021355	0.008717	-0.024184	0.025976	-0.130016
JobLevel	-0.007295	-0.021557	0.023433	-0.006157	-0.107830	0.114307	0.010409	-0.008431	0.092698	-0.014114	-0.016724	-0.054707	0.039403	-0.039403	0.115704	-0.100922	-0.344608	0.552744	0.114896	0.414319	-0.387788	0.127490	-0.216559	0.037087	0.050547	-0.087072	NaN	-0.000544	0.000544	-0.423418	-0.071124	0.436593	0.509604	0.002966	0.005303	0.101589	NaN	-0.018519	0.001212	-0.027853	-0.012630	1.000000	-0.001944	0.950300	0.039563	0.142501	-0.034730	-0.021222	0.021642	NaN	0.013984	0.782208	-0.018191	0.037818	0.534739	0.389447	0.353885	0.375281	-0.169105
JobSatisfaction	0.019802	0.027117	-0.036562	-0.024068	-0.002798	0.013499	-0.021467	0.052004	-0.023528	-0.022645	0.003380	-0.019795	-0.033252	0.033252	0.016367	-0.029681	-0.015710	-0.005620	-0.013747	-0.006217	0.020503	0.012604	0.001413	-0.015197	-0.010315	0.024571	NaN	-0.024539	0.024539	0.007416	0.034986	-0.012984	-0.004892	0.030571	-0.003669	-0.011296	NaN	-0.046247	-0.006784	-0.071335	-0.021476	-0.001944	1.000000	-0.007157	0.000644	-0.055699	0.020002	0.002297	-0.012454	NaN	0.010690	-0.020185	-0.005779	-0.019459	-0.003803	-0.002305	-0.018214	-0.027656	-0.103481
MonthlyIncome	-0.017261	-0.031658	0.038779	0.006815	-0.064720	0.063978	0.021456	-0.007054	0.062576	0.001025	-0.022279	-0.049695	0.031858	-0.031858	0.068177	-0.092250	-0.320906	0.619573	0.055684	0.485818	-0.345180	0.047792	-0.201514	0.032203	0.056767	-0.089361	NaN	-0.006089	0.006089	-0.423949	-0.078345	0.438270	0.497855	0.007707	-0.017014	0.094961	NaN	-0.014829	-0.006259	-0.015794	-0.015271	0.950300	-0.007157	1.000000	0.034814	0.149515	-0.027269	-0.017120	0.025873	NaN	0.005408	0.772893	-0.021736	0.030683	0.514285	0.363818	0.344978	0.344079	-0.159840
MonthlyRate	0.015279	0.000344	-0.010484	-0.024390	-0.005453	0.016388	0.009567	0.025545	-0.011559	-0.001723	-0.035606	-0.004535	0.041482	-0.041482	0.003829	-0.027470	-0.016056	0.031717	0.007711	0.025875	-0.027008	0.011854	-0.001200	-0.000227	-0.034689	0.037260	NaN	-0.021431	0.021431	-0.018712	0.001694	0.018529	0.028051	-0.032182	0.027473	-0.026084	NaN	0.012648	0.037600	-0.015297	-0.016322	0.039563	0.000644	0.034814	1.000000	0.017521	-0.006429	-0.009811	-0.004085	NaN	-0.034323	0.026442	0.001467	0.007963	-0.023655	-0.012815	0.001567	-0.036746	0.015170
NumCompaniesWorked	0.002718	-0.039718	0.032401	0.020618	0.022237	-0.032097	0.031007	-0.006131	-0.018611	0.024826	-0.012870	-0.013819	0.039147	-0.039147	0.026955	0.020578	-0.021121	0.042125	0.009580	0.097925	-0.043981	0.005913	-0.104494	0.040824	-0.016142	-0.019161	NaN	0.020786	-0.020786	-0.226912	-0.050157	0.235877	0.299635	0.038153	-0.029251	0.126317	NaN	-0.001251	0.012594	0.022157	0.015012	0.142501	-0.055699	0.149515	0.017521	1.000000	-0.010238	-0.014095	0.052733	NaN	0.030075	0.237639	-0.066054	-0.008366	-0.118421	-0.090754	-0.036814	-0.110319	0.043494
PercentSalaryHike	0.036591	-0.006675	-0.018649	-0.025888	0.030735	-0.020403	-0.016142	0.010209	-0.027726	0.029116	0.019297	-0.042701	-0.002733	0.002733	0.020591	-0.021032	-0.020628	-0.005394	0.034682	-0.017017	0.032537	-0.046683	0.031102	-0.023478	0.020895	-0.001386	NaN	0.005433	-0.005433	0.001236	-0.032036	0.003827	0.003634	0.022704	0.040235	-0.011111	NaN	-0.012944	-0.031701	-0.009062	-0.017205	-0.034730	0.020002	-0.027269	-0.006429	-0.010238	1.000000	0.773550	-0.040490	NaN	0.007528	-0.020608	-0.005221	-0.003280	-0.035991	-0.001520	-0.022154	-0.011985	-0.013478
PerformanceRating	0.018310	0.016463	-0.026390	-0.006385	0.032720	-0.031050	-0.016167	0.010853	-0.020918	0.014868	0.011449	-0.021729	0.013859	-0.013859	-0.000928	-0.010154	0.010796	0.032050	0.029775	-0.035744	0.019416	-0.041401	-0.006214	-0.010310	0.009585	-0.001045	NaN	-0.004369	0.004369	0.002906	-0.031529	0.002069	0.001904	0.000473	0.027110	-0.024539	NaN	-0.020359	-0.029548	-0.002172	-0.029071	-0.021222	0.002297	-0.017120	-0.009811	-0.014095	0.773550	1.000000	-0.031351	NaN	0.003506	0.006744	-0.015579	0.002572	0.003435	0.034986	0.017896	0.022827	0.002889
RelationshipSatisfaction	0.021132	0.028500	-0.038640	0.034583	-0.004587	-0.010489	0.041105	-0.019973	-0.006580	0.030494	-0.020305	-0.011044	-0.022868	0.022868	-0.005090	0.044169	-0.010691	0.025638	0.003640	-0.005492	-0.003116	-0.004836	-0.024859	0.006199	-0.043382	0.040817	NaN	-0.048493	0.048493	-0.052690	0.028249	0.048460	0.053535	0.007846	0.006557	-0.009118	NaN	-0.069861	0.007665	0.001330	0.034297	0.021642	-0.012454	0.025873	-0.004085	0.052733	-0.040490	-0.031351	1.000000	NaN	-0.045952	0.024054	0.002497	0.019604	0.019367	-0.015123	0.033493	-0.000867	-0.045872
StandardHours	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
StockOptionLevel	0.028807	-0.016142	-0.005303	-0.004000	0.016927	-0.015755	0.021206	-0.017993	0.022560	0.033750	-0.042100	-0.024560	-0.012716	0.012716	0.014021	-0.009864	0.013386	-0.015637	0.007735	0.015807	-0.011635	0.015756	-0.048067	0.446285	0.225574	-0.638957	NaN	0.000449	-0.000449	0.011119	-0.068944	-0.000263	0.037510	0.042143	0.044872	0.018422	NaN	0.062227	0.003432	0.050263	0.021523	0.013984	0.010690	0.005408	-0.034323	0.030075	0.007528	0.003506	-0.045952	NaN	1.000000	0.010136	0.011274	0.004129	0.015058	0.050818	0.014352	0.024698	-0.137145
TotalWorkingYears	-0.029742	-0.012177	0.030320	0.007508	0.011087	-0.014781	0.005505	-0.003630	0.025779	0.024890	-0.028935	-0.041577	0.046881	-0.046881	0.112159	-0.076482	-0.215426	0.465837	0.064077	0.312148	-0.228119	-0.012241	-0.207726	0.036291	0.053512	-0.089529	NaN	-0.012754	0.012754	-0.543259	-0.107273	0.562698	0.680381	0.014515	0.004628	0.148280	NaN	-0.014365	-0.002693	-0.002334	-0.005533	0.782208	-0.020185	0.772893	0.026442	0.237639	-0.020608	0.006744	0.024054	NaN	0.010136	1.000000	-0.035662	0.001008	0.628133	0.460365	0.404858	0.459188	-0.171063
TrainingTimesLastYear	-0.020746	0.006193	0.008498	-0.040022	-0.006819	0.024688	-0.037664	-0.039018	-0.029046	0.070542	-0.008151	0.008289	0.038787	-0.038787	-0.012432	-0.035902	0.053998	0.003052	-0.013987	-0.004527	-0.052126	0.013241	0.040377	0.008405	-0.029602	0.024129	NaN	0.079113	-0.079113	0.002348	-0.017179	0.000359	-0.019621	0.002453	-0.036942	-0.025100	NaN	0.023603	-0.019359	-0.008548	-0.015338	-0.018191	-0.005779	-0.021736	0.001467	-0.066054	-0.005221	-0.015579	0.002497	NaN	0.011274	-0.035662	1.000000	0.028072	0.003569	-0.005738	-0.002067	-0.004096	-0.059478
WorkLifeBalance	0.005780	0.010199	-0.012640	0.047763	-0.069922	0.051320	-0.003967	-0.039728	0.018500	0.001641	0.031812	0.021962	0.002753	-0.002753	-0.026101	0.043887	-0.028209	0.005137	0.002011	0.034403	-0.058613	0.032092	0.045148	-0.009080	-0.006388	0.014921	NaN	0.027092	-0.027092	0.000122	0.025010	-0.004079	-0.021490	-0.037848	-0.026556	0.009819	NaN	0.010309	0.027627	-0.004607	-0.014617	0.037818	-0.019459	0.030683	0.007963	-0.008366	-0.003280	0.002572	0.019604	NaN	0.004129	0.001008	0.028072	1.000000	0.012089	0.049856	0.008941	0.002759	-0.063939
YearsAtCompany	0.007623	0.012991	-0.016274	0.007944	-0.032181	0.029805	-0.005146	-0.002019	0.031365	0.010805	-0.030331	-0.021399	0.029747	-0.029747	0.069758	-0.052569	-0.150181	0.330965	0.031968	0.153918	-0.154062	0.042602	-0.163464	0.025728	0.044925	-0.070935	NaN	0.011687	-0.011687	-0.226865	-0.084647	0.241287	0.311309	-0.034055	0.009508	0.069114	NaN	-0.011240	0.001458	-0.019582	-0.021355	0.534739	-0.003803	0.514285	-0.023655	-0.118421	-0.035991	0.003435	0.019367	NaN	0.015058	0.628133	0.003569	0.012089	1.000000	0.758754	0.618409	0.769212	-0.134392
YearsInCurrentRole	0.011549	0.001680	-0.009147	-0.040287	-0.028151	0.046883	-0.025443	0.018343	0.025126	-0.026418	-0.017021	0.009683	0.041483	-0.041483	0.054695	-0.057876	-0.131322	0.167499	0.067877	0.136332	-0.131314	0.092349	-0.149751	0.018532	0.065488	-0.086486	NaN	0.029758	-0.029758	-0.125668	-0.086377	0.139904	0.212901	0.009932	0.018845	0.060236	NaN	-0.008416	0.018007	-0.024106	0.008717	0.389447	-0.002305	0.363818	-0.012815	-0.090754	-0.001520	0.034986	-0.015123	NaN	0.050818	0.460365	-0.005738	0.049856	0.758754	1.000000	0.548056	0.714365	-0.160545
YearsSinceLastPromotion	0.020815	0.023216	-0.033877	-0.026931	-0.021497	0.034112	-0.023700	-0.002480	0.006219	0.022665	-0.039931	0.003853	0.026985	-0.026985	0.075902	-0.054603	-0.110099	0.224255	-0.007241	0.074455	-0.105237	0.049202	-0.085622	-0.005279	0.054102	-0.053090	NaN	0.012239	-0.012239	-0.147455	-0.050238	0.156073	0.216513	-0.033229	0.010029	0.054254	NaN	-0.009019	0.016194	-0.026716	-0.024184	0.353885	-0.018214	0.344978	0.001567	-0.036814	-0.022154	0.017896	0.033493	NaN	0.014352	0.404858	-0.002067	0.008941	0.618409	0.548056	1.000000	0.510224	-0.033019
YearsWithCurrManager	0.016716	0.012628	-0.022023	-0.027079	-0.014963	0.027415	-0.028868	0.003636	0.033418	-0.011544	-0.011714	-0.000836	0.030599	-0.030599	0.039407	-0.051006	-0.107072	0.164695	0.076207	0.131279	-0.127608	0.083028	-0.168743	0.014095	0.032972	-0.047793	NaN	0.041586	-0.041586	-0.113396	-0.085508	0.127439	0.202089	-0.026363	0.014406	0.069065	NaN	-0.009197	-0.004999	-0.020123	0.025976	0.375281	-0.027656	0.344079	-0.036746	-0.110319	-0.011985	0.022827	-0.000867	NaN	0.024698	0.459188	-0.004096	0.002759	0.769212	0.714365	0.510224	1.000000	-0.156199
Attrition	-0.074457	0.115143	-0.049538	0.016832	-0.085293	0.080855	0.036466	-0.032703	0.055781	-0.046999	-0.017898	0.069355	-0.029453	0.029453	-0.078696	0.036215	0.098290	-0.083316	-0.082994	-0.088870	-0.000360	0.019774	0.157234	-0.087716	-0.090984	0.175419	NaN	-0.246118	0.246118	0.080224	0.068147	-0.091370	-0.159205	-0.056652	0.077924	-0.031373	NaN	-0.010577	-0.103369	-0.006846	-0.130016	-0.169105	-0.103481	-0.159840	0.015170	0.043494	-0.013478	0.002889	-0.045872	NaN	-0.137145	-0.171063	-0.059478	-0.063939	-0.134392	-0.160545	-0.033019	-0.156199	1.000000

plt.figure(figsize=(100,100))
sns.heatmap(final_df.corr(),annot=True)

<AxesSubplot:>

4) Explore the Data using Exploratory Data Analysis - For pairs of Y and all Xs

final_df.corr()['Attrition']

BusinessTravel_Non-Travel           -0.074457
BusinessTravel_Travel_Frequently     0.115143
BusinessTravel_Travel_Rarely        -0.049538
Department_Human Resources           0.016832
Department_Research & Development   -0.085293
Department_Sales                     0.080855
EducationField_Human Resources       0.036466
EducationField_Life Sciences        -0.032703
EducationField_Marketing             0.055781
EducationField_Medical              -0.046999
EducationField_Other                -0.017898
EducationField_Technical Degree      0.069355
Gender_Female                       -0.029453
Gender_Male                          0.029453
JobRole_Healthcare Representative   -0.078696
JobRole_Human Resources              0.036215
JobRole_Laboratory Technician        0.098290
JobRole_Manager                     -0.083316
JobRole_Manufacturing Director      -0.082994
JobRole_Research Director           -0.088870
JobRole_Research Scientist          -0.000360
JobRole_Sales Executive              0.019774
JobRole_Sales Representative         0.157234
MaritalStatus_Divorced              -0.087716
MaritalStatus_Married               -0.090984
MaritalStatus_Single                 0.175419
Over18_Y                                  NaN
OverTime_No                         -0.246118
OverTime_Yes                         0.246118
Age Group_Adult                      0.080224
Age Group_Child                      0.068147
Age Group_Old                       -0.091370
Age                                 -0.159205
DailyRate                           -0.056652
DistanceFromHome                     0.077924
Education                           -0.031373
EmployeeCount                             NaN
EmployeeNumber                      -0.010577
EnvironmentSatisfaction             -0.103369
HourlyRate                          -0.006846
JobInvolvement                      -0.130016
JobLevel                            -0.169105
JobSatisfaction                     -0.103481
MonthlyIncome                       -0.159840
MonthlyRate                          0.015170
NumCompaniesWorked                   0.043494
PercentSalaryHike                   -0.013478
PerformanceRating                    0.002889
RelationshipSatisfaction            -0.045872
StandardHours                             NaN
StockOptionLevel                    -0.137145
TotalWorkingYears                   -0.171063
TrainingTimesLastYear               -0.059478
WorkLifeBalance                     -0.063939
YearsAtCompany                      -0.134392
YearsInCurrentRole                  -0.160545
YearsSinceLastPromotion             -0.033019
YearsWithCurrManager                -0.156199
Attrition                            1.000000
Name: Attrition, dtype: float64

5) Visualize all the distribution, relationships

plt.figure(figsize=(20,40))
x=1
for i in df.columns:
    if(df[i].dtypes=="object"):
        plt.subplot(7,5,x)
        sns.countplot(df[i])
        x=x+1
    else:
        plt.subplot(7,5,x)
        sns.distplot(df[i])
        x=x+1

plt.figure(figsize=(20,40))
x=1
for i in df.columns:
    if(df[i].dtypes=="object"):
        plt.subplot(7,5,x)
        sns.countplot(df[i],hue=df.Attrition)
        x=x+1
    else:
        plt.subplot(7,5,x)
        sns.scatterplot(df[i],df['Attrition'])
        x=x+1

sns.pairplot(df)

<seaborn.axisgrid.PairGrid at 0x2ead201f580>

6) Perform Test of Hypothesis - Compare rates for same level male and female, check relationship between categorical variables like Age and Gender/Gender and Education Field, Age and Income, etc

df['Age'].unique()

array([41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 29, 31, 34, 28, 22, 53,
       24, 21, 42, 44, 46, 39, 43, 50, 26, 48, 55, 45, 56, 23, 51, 40, 54,
       58, 20, 25, 19, 57, 52, 47, 18, 60], dtype=int64)

df['Gender'].unique()

array(['Female', 'Male'], dtype=object)

df['Gender'].value_counts()

Male      882
Female    588
Name: Gender, dtype: int64

Cross tabulation

AG = pd.crosstab(df['Age'],df['Gender'])
AG

Gender	Female	Male
Age
18	4	4
19	4	5
20	6	5
21	6	7
22	5	11
23	1	13
24	11	15
25	6	20
26	14	25
27	22	26
28	12	36
29	29	39
30	26	34
31	28	41
32	23	38
33	24	34
34	33	44
35	27	51
36	29	40
37	18	32
38	27	31
39	16	26
40	24	33
41	15	25
42	16	30
43	14	18
44	10	23
45	17	24
46	15	18
47	14	10
48	7	12
49	12	12
50	9	21
51	7	12
52	7	11
53	10	9
54	15	3
55	4	18
56	7	7
57	0	4
58	6	8
59	6	4
60	2	3

from scipy.stats import chi2_contingency

chi_sqr, p_value, DOF, EXP  = chi2_contingency(AG)

chi_sqr

53.537114950915246

p_value

0.10927801670328505

df['Gender'].unique()

array(['Female', 'Male'], dtype=object)

df['EducationField'].unique()

array(['Life Sciences', 'Other', 'Medical', 'Marketing',
       'Technical Degree', 'Human Resources'], dtype=object)

df['Gender'].value_counts()

Male      882
Female    588
Name: Gender, dtype: int64

df['EducationField'].value_counts()

Life Sciences       606
Medical             464
Marketing           159
Technical Degree    132
Other                82
Human Resources      27
Name: EducationField, dtype: int64

GE = pd.crosstab(df['Gender'],df['EducationField'])
GE

EducationField	Human Resources	Life Sciences	Marketing	Medical	Other	Technical Degree
Gender
Female	8	240	69	190	29	52
Male	19	366	90	274	53	80

chi_sqr, p_value, DOF, EXP  = chi2_contingency(GE)

chi_sqr

2.9414238793151797

p_value

0.7090162522843911

df['Age'].unique()

array([41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 29, 31, 34, 28, 22, 53,
       24, 21, 42, 44, 46, 39, 43, 50, 26, 48, 55, 45, 56, 23, 51, 40, 54,
       58, 20, 25, 19, 57, 52, 47, 18, 60], dtype=int64)

df['MonthlyIncome'].unique()

array([5993, 5130, 2090, ..., 9991, 5390, 4404], dtype=int64)

df['MonthlyIncome'].value_counts()

2342     4
6142     3
2610     3
2559     3
6347     3
        ..
4103     1
2705     1
6796     1
19717    1
10239    1
Name: MonthlyIncome, Length: 1349, dtype: int64

AMI = pd.crosstab(df['Age'],df['MonthlyIncome'])
AMI

MonthlyIncome	1009	1051	1052	1081	1091	1102	1118	1129	1200	1223	1232	1261	1274	1281	1359	1393	1416	1420	1483	1514	1555	1563	1569	1601	1611	1675	1702	1706	1790	1859	1878	1904	1951	2001	2007	2008	2011	2013	2014	2018	2022	2024	2028	2029	2033	2042	2044	2045	2058	2061	...	19033	19038	19045	19049	19068	19081	19094	19141	19144	19161	19187	19189	19190	19197	19202	19232	19237	19246	19272	19328	19331	19392	19406	19419	19431	19436	19502	19513	19517	19537	19545	19566	19586	19613	19626	19627	19636	19658	19665	19701	19717	19740	19833	19845	19847	19859	19926	19943	19973	19999
Age
18	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
19	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
20	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
21	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
23	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
24	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
25	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
26	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
27	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
28	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
29	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
30	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
31	0	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
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43 rows × 1349 columns

chi_sqr, p_value, DOF, EXP  = chi2_contingency(AMI)

chi_sqr

57003.01607044047

p_value

0.12517635416744993

7) Perform Regression treating Monthly Rate as Y, and choose prediction error and the best model

final_df.corr()['MonthlyRate']

BusinessTravel_Non-Travel            0.015279
BusinessTravel_Travel_Frequently     0.000344
BusinessTravel_Travel_Rarely        -0.010484
Department_Human Resources          -0.024390
Department_Research & Development   -0.005453
Department_Sales                     0.016388
EducationField_Human Resources       0.009567
EducationField_Life Sciences         0.025545
EducationField_Marketing            -0.011559
EducationField_Medical              -0.001723
EducationField_Other                -0.035606
EducationField_Technical Degree     -0.004535
Gender_Female                        0.041482
Gender_Male                         -0.041482
JobRole_Healthcare Representative    0.003829
JobRole_Human Resources             -0.027470
JobRole_Laboratory Technician       -0.016056
JobRole_Manager                      0.031717
JobRole_Manufacturing Director       0.007711
JobRole_Research Director            0.025875
JobRole_Research Scientist          -0.027008
JobRole_Sales Executive              0.011854
JobRole_Sales Representative        -0.001200
MaritalStatus_Divorced              -0.000227
MaritalStatus_Married               -0.034689
MaritalStatus_Single                 0.037260
Over18_Y                                  NaN
OverTime_No                         -0.021431
OverTime_Yes                         0.021431
Age Group_Adult                     -0.018712
Age Group_Child                      0.001694
Age Group_Old                        0.018529
Age                                  0.028051
DailyRate                           -0.032182
DistanceFromHome                     0.027473
Education                           -0.026084
EmployeeCount                             NaN
EmployeeNumber                       0.012648
EnvironmentSatisfaction              0.037600
HourlyRate                          -0.015297
JobInvolvement                      -0.016322
JobLevel                             0.039563
JobSatisfaction                      0.000644
MonthlyIncome                        0.034814
MonthlyRate                          1.000000
NumCompaniesWorked                   0.017521
PercentSalaryHike                   -0.006429
PerformanceRating                   -0.009811
RelationshipSatisfaction            -0.004085
StandardHours                             NaN
StockOptionLevel                    -0.034323
TotalWorkingYears                    0.026442
TrainingTimesLastYear                0.001467
WorkLifeBalance                      0.007963
YearsAtCompany                      -0.023655
YearsInCurrentRole                  -0.012815
YearsSinceLastPromotion              0.001567
YearsWithCurrManager                -0.036746
Attrition                            0.015170
Name: MonthlyRate, dtype: float64

final_df

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager	Attrition
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834	1
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541	0
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935	1
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935	0
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873	0
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541	0
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873	0
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895	0
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227	0

1470 rows × 59 columns

nonCorrWithmonthlyrate = [column for column in final_df if abs(final_df[column].corr(final_df["MonthlyRate"])) < 0.01]
final_df1 = final_df.drop(nonCorrWithmonthlyrate, axis=1)
final_df1

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Sales	EducationField_Life Sciences	EducationField_Marketing	EducationField_Other	Gender_Female	Gender_Male	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	MonthlyIncome	MonthlyRate	NumCompaniesWorked	StandardHours	StockOptionLevel	TotalWorkingYears	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager	Attrition
0	0	1	0	1	1	0	0	1	0	0	0	0	0	0	1	0	1	1	0	1	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	-0.108350	0.726020	2.125136	0.0	-0.932014	-0.421642	-0.164613	-0.063296	0.245834	1
1	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	1	1	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.291719	1.488876	-0.678049	0.0	0.241988	-0.164511	0.488508	0.764998	0.806541	0
2	0	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	1	1	0	1	1	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	-0.937654	-1.674841	1.324226	0.0	-0.932014	-0.550208	-1.144294	-1.167687	-1.155935	1
3	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	1	0	1	1	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	-0.763634	1.243211	-0.678049	0.0	-0.932014	-0.421642	0.161947	0.764998	-1.155935	0
4	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	1	0	1	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.644858	0.325900	2.525591	0.0	0.241988	-0.678774	-0.817734	-0.615492	-0.595227	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	1	0	1	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	-0.835451	-0.284329	0.523316	0.0	0.241988	0.735447	-0.327893	-0.615492	-0.314873	0
1466	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	1	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	0.741140	1.004010	0.523316	0.0	0.241988	-0.293077	-0.001333	0.764998	0.806541	0
1467	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	1	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.076690	-1.284418	-0.678049	0.0	0.241988	-0.678774	-0.164613	-0.615492	-0.314873	0
1468	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	1	0	1	1	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.236474	-0.150393	-0.277594	0.0	-0.932014	0.735447	0.325228	0.488900	1.086895	0
1469	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	1	0	1	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	-0.445978	-0.574124	-0.277594	0.0	-0.932014	-0.678774	-0.491174	-0.339394	-0.595227	0

1470 rows × 42 columns

Final_df = final_df1.drop(["MonthlyRate"],axis=1)

Final_df

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Sales	EducationField_Life Sciences	EducationField_Marketing	EducationField_Other	Gender_Female	Gender_Male	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	MonthlyIncome	NumCompaniesWorked	StandardHours	StockOptionLevel	TotalWorkingYears	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager	Attrition
0	0	1	0	1	1	0	0	1	0	0	0	0	0	0	1	0	1	1	0	1	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	-0.108350	2.125136	0.0	-0.932014	-0.421642	-0.164613	-0.063296	0.245834	1
1	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	1	0	1	1	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.291719	-0.678049	0.0	0.241988	-0.164511	0.488508	0.764998	0.806541	0
2	0	1	0	0	0	0	1	0	1	0	1	0	0	0	0	0	1	1	0	1	1	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	-0.937654	1.324226	0.0	-0.932014	-0.550208	-1.144294	-1.167687	-1.155935	1
3	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	1	0	1	0	1	1	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	-0.763634	-0.678049	0.0	-0.932014	-0.421642	0.161947	0.764998	-1.155935	0
4	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	1	0	1	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.644858	2.525591	0.0	0.241988	-0.678774	-0.817734	-0.615492	-0.595227	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	1	0	1	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	-0.835451	0.523316	0.0	0.241988	0.735447	-0.327893	-0.615492	-0.314873	0
1466	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	1	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	0.741140	0.523316	0.0	0.241988	-0.293077	-0.001333	0.764998	0.806541	0
1467	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	1	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.076690	-0.678049	0.0	0.241988	-0.678774	-0.164613	-0.615492	-0.314873	0
1468	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	1	0	1	1	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.236474	-0.277594	0.0	-0.932014	0.735447	0.325228	0.488900	1.086895	0
1469	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	1	1	0	1	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	-0.445978	-0.277594	0.0	-0.932014	-0.678774	-0.491174	-0.339394	-0.595227	0

1470 rows × 41 columns

Multicolinearity

from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame()
vif['Features'] = Final_df.columns
vif['VIF'] = [variance_inflation_factor(Final_df.values,i) for i in range(Final_df.shape[1])]
vif

	Features	VIF
0	BusinessTravel_Non-Travel	1.443079
1	BusinessTravel_Travel_Rarely	1.414884
2	Department_Human Resources	6.376036
3	Department_Sales	4.893546
4	EducationField_Life Sciences	1.187202
5	EducationField_Marketing	1.581020
6	EducationField_Other	1.096789
7	Gender_Female	inf
8	Gender_Male	inf
9	JobRole_Human Resources	6.412652
10	JobRole_Laboratory Technician	2.273979
11	JobRole_Manager	3.481272
12	JobRole_Research Director	2.353438
13	JobRole_Research Scientist	2.411984
14	JobRole_Sales Executive	3.993258
15	MaritalStatus_Married	1.854259
16	MaritalStatus_Single	3.062720
17	Over18_Y	0.000000
18	OverTime_No	inf
19	OverTime_Yes	inf
20	Age Group_Adult	43.488194
21	Age Group_Old	49.162061
22	Age	4.418960
23	DailyRate	1.030049
24	DistanceFromHome	1.028407
25	Education	1.092031
26	EmployeeCount	NaN
27	EmployeeNumber	1.026819
28	EnvironmentSatisfaction	1.041506
29	HourlyRate	1.019807
30	JobInvolvement	1.046943
31	JobLevel	14.048080
32	MonthlyIncome	18.039304
33	NumCompaniesWorked	1.286700
34	StandardHours	NaN
35	StockOptionLevel	1.915341
36	TotalWorkingYears	4.988296
37	YearsAtCompany	4.392647
38	YearsInCurrentRole	2.710913
39	YearsWithCurrManager	2.829019
40	Attrition	1.280857

featurestodrop = vif.loc[vif['VIF']>10]
droplist = featurestodrop['Features']
droplist = list(droplist)
len(droplist)
print(droplist)

['Gender_Female', 'Gender_Male', 'OverTime_No', 'OverTime_Yes', 'Age Group_Adult', 'Age Group_Old', 'JobLevel', 'MonthlyIncome']

df_Att= Final_df.drop(droplist,axis=1)
df_Att

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Sales	EducationField_Life Sciences	EducationField_Marketing	EducationField_Other	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	NumCompaniesWorked	StandardHours	StockOptionLevel	TotalWorkingYears	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager	Attrition
0	0	1	0	1	1	0	0	0	0	0	0	0	1	0	1	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	2.125136	0.0	-0.932014	-0.421642	-0.164613	-0.063296	0.245834	1
1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.678049	0.0	0.241988	-0.164511	0.488508	0.764998	0.806541	0
2	0	1	0	0	0	0	1	0	1	0	0	0	0	0	1	1	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	1.324226	0.0	-0.932014	-0.550208	-1.144294	-1.167687	-1.155935	1
3	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.678049	0.0	-0.932014	-0.421642	0.161947	0.764998	-1.155935	0
4	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	2.525591	0.0	0.241988	-0.678774	-0.817734	-0.615492	-0.595227	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	1	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	0.523316	0.0	0.241988	0.735447	-0.327893	-0.615492	-0.314873	0
1466	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.523316	0.0	0.241988	-0.293077	-0.001333	0.764998	0.806541	0
1467	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	1	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.678049	0.0	0.241988	-0.678774	-0.164613	-0.615492	-0.314873	0
1468	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.277594	0.0	-0.932014	0.735447	0.325228	0.488900	1.086895	0
1469	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.277594	0.0	-0.932014	-0.678774	-0.491174	-0.339394	-0.595227	0

1470 rows × 33 columns

x = df_Att

x

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Sales	EducationField_Life Sciences	EducationField_Marketing	EducationField_Other	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	NumCompaniesWorked	StandardHours	StockOptionLevel	TotalWorkingYears	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager	Attrition
0	0	1	0	1	1	0	0	0	0	0	0	0	1	0	1	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	2.125136	0.0	-0.932014	-0.421642	-0.164613	-0.063296	0.245834	1
1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.678049	0.0	0.241988	-0.164511	0.488508	0.764998	0.806541	0
2	0	1	0	0	0	0	1	0	1	0	0	0	0	0	1	1	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	1.324226	0.0	-0.932014	-0.550208	-1.144294	-1.167687	-1.155935	1
3	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.678049	0.0	-0.932014	-0.421642	0.161947	0.764998	-1.155935	0
4	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	2.525591	0.0	0.241988	-0.678774	-0.817734	-0.615492	-0.595227	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	1	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	0.523316	0.0	0.241988	0.735447	-0.327893	-0.615492	-0.314873	0
1466	0	1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.523316	0.0	0.241988	-0.293077	-0.001333	0.764998	0.806541	0
1467	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	1	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.678049	0.0	0.241988	-0.678774	-0.164613	-0.615492	-0.314873	0
1468	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.277594	0.0	-0.932014	0.735447	0.325228	0.488900	1.086895	0
1469	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.277594	0.0	-0.932014	-0.678774	-0.491174	-0.339394	-0.595227	0

1470 rows × 33 columns

y = df[['MonthlyRate']]

y

	MonthlyRate
0	19479
1	24907
2	2396
3	23159
4	16632
...	...
1465	12290
1466	21457
1467	5174
1468	13243
1469	10228

1470 rows × 1 columns

Divide data in training & testing set

xtrain,xtest,ytrain,ytest= train_test_split(x,y,test_size=0.25,random_state=25)

Model Building

Linear Regression

lin_model = LinearRegression()
lin_model.fit(xtrain,ytrain)

LinearRegression()

# Training data evaluation
ypredtrain = lin_model.predict(xtrain)

MAE = mean_absolute_error(ytrain,ypredtrain)
print('Mean absolute error:',MAE)

acc_linear = r2_score(ytrain,ypredtrain)
print('Training Accuracy:',acc_linear)

Mean absolute error: 6123.968239564429
Training Accuracy: 0.01751349297820326

# Testing data evaluation
ypredtest = lin_model.predict(xtest)

MAE = mean_absolute_error(ytest,ypredtest)
print('Mean absolute error:',MAE)

acc_linear = r2_score(ytest,ypredtest)
print('Testing Accuracy:',acc_linear)

Mean absolute error: 6164.817934782609
Testing Accuracy: -0.01650754483686856

Lasso Regression

lasso_model = Lasso()
lasso_model.fit(xtrain,ytrain)

Lasso()

# Training data evaluation
ypredtrain = lasso_model.predict(xtrain)

MAE = mean_absolute_error(ytrain,ypredtrain)
print('Mean absolute error:',MAE)

acc_lasso = r2_score(ytrain,ypredtrain)
print('Training Accuracy:',acc_lasso)

Mean absolute error: 6121.1571988063115
Training Accuracy: 0.01934535595164133

# Testing data evaluation
ypredtest = lasso_model.predict(xtest)

MAE = mean_absolute_error(ytest,ypredtest)
print('Mean absolute error:',MAE)

acc_lasso = r2_score(ytest,ypredtest)
print('Testing Accuracy:',acc_lasso)

Mean absolute error: 6189.8646400047155
Testing Accuracy: -0.02333262771485556

Ridge Regression

ridge_model = Ridge()
ridge_model.fit(xtrain,ytrain)

Ridge()

# Training data evaluation
ypredtrain = ridge_model.predict(xtrain)

Mean_absolute_error = mean_absolute_error(ytrain,ypredtrain)
print('Mean_absolute_error:',Mean_absolute_error)

acc_ridge = r2_score(ytrain,ypredtrain)
print('Training Accuracy:',acc_ridge)

Mean_absolute_error: 6121.036816827892
Training Accuracy: 0.01934582713731403

# Testing data evaluation
ypredtest = ridge_model.predict(xtest)

Mean_absolute_error = mean_absolute_error(ytest,ypredtest)
print('Mean_absolute_error:',Mean_absolute_error)

acc_ridge = r2_score(ytest,ypredtest)
print('Testing Accuracy:',acc_ridge)

Mean_absolute_error: 6190.905298336976
Testing Accuracy: -0.023655552374451494

KNeighborsRegressor

from sklearn.neighbors import KNeighborsRegressor

KNN_model = KNeighborsRegressor()
KNN_model.fit(xtrain,ytrain)

KNeighborsRegressor()

# Training data evaluation
ypredtrain = KNN_model.predict(xtrain)

acc_KNR = r2_score(ytrain,ypredtrain)
print('Training accuracy:',acc_KNR )

Training accuracy: 0.21360805483649958

# Testing data evaluation
ypredtest = KNN_model.predict(xtest)
# ypredtest

acc_KNR  = r2_score(ytest,ypredtest)
print('Testing accuracy:',acc_KNR )

Testing accuracy: -0.15435468903713745

Decision Tree

from sklearn.tree import DecisionTreeRegressor, plot_tree

dt_reg = DecisionTreeRegressor()
dt_reg.fit(xtrain, ytrain)

DecisionTreeRegressor()

ypredtrain = dt_reg.predict(xtrain)

mse = mean_squared_error(ytrain, ypredtrain)
print("MSE :",mse)

rmse = np.sqrt(mse)
print("RMSE :",rmse)

acc_DT = r2_score(ytrain, ypredtrain)
print("Training accuracy :",acc_DT)

MSE : 0.0
RMSE : 0.0
Training accuracy : 1.0

ypred = dt_reg.predict(xtest)

mse = mean_squared_error(ytest, ypred)
print("MSE :",mse)

rmse = np.sqrt(mse)
print("RMSE :",rmse)

acc_DT = r2_score(ytest, ypred)
print("Testing accuracy :",acc_DT)

MSE : 101312354.08152173
RMSE : 10065.403821085458
Testing accuracy : -1.0247599731746173

# dt_reg = DecisionTreeRegressor()

# hyp_grid = {'criterion':['mse','mae'],
#             'max_depth':np.arange(3,15),
#             'min_samples_split':np.arange(2,20),
#             'min_samples_leaf':np.arange(2,15)}

# gscv_dt_reg = GridSearchCV(dt_reg,hyp_grid,cv=5)
# gscv_dt_reg.fit(xtrain,ytrain)

# gscv_dt_reg.best_estimator_

# dt_reg = DecisionTreeRegressor(criterion='mae', max_depth=3, min_samples_leaf=2, min_samples_split=5)
# dt_reg.fit(xtrain, ytrain)

# ypredtrain = dt_reg.predict(xtrain)

# mse = mean_squared_error(ytrain, ypredtrain)
# print("MSE :",mse)

# rmse = np.sqrt(mse)
# print("RMSE :",rmse)

# acc_DT = r2_score(ytrain, ypredtrain)
# print("Training accuracy :",acc_DT)

# ypred = dt_reg.predict(xtest)

# mse = mean_squared_error(ytest, ypred)
# print("MSE :",mse)

# rmse = np.sqrt(mse)
# print("RMSE :",rmse)

# acc_DT = r2_score(ytest, ypred)
# print("Testing accuracy :",acc_DT)

# plt.figure(figsize=(200,150))
# tree = plot_tree(dt_reg,feature_names=x.columns,filled=True)
# plt.savefig('dt_reg.png')

8) Form Classification Model using Y=attrition and choose the best model

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,recall_score,precision_score,f1_score

final_df

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager	Attrition
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834	1
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541	0
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935	1
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935	0
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873	0
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541	0
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873	0
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895	0
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227	0

1470 rows × 59 columns

Final_df_class = final_df.drop(["Attrition"],axis=1)

Final_df_class

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227

1470 rows × 58 columns

x = Final_df_class

x

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227

1470 rows × 58 columns

 y = df[['Attrition']]

y

	Attrition
0	1
1	0
2	1
3	0
4	0
...	...
1465	0
1466	0
1467	0
1468	0
1469	0

1470 rows × 1 columns

Divide data in training & testing set

xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=25,stratify=y)

Model Building

Logistic Regression

log_model = LogisticRegression()
log_model.fit(xtrain,ytrain)

LogisticRegression()

# Training data evaluation
ypredtrain = log_model.predict(xtrain)

Accuracy_log = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_log)

confusionmatrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion matrix: \n',confusionmatrix)

Precision = precision_score(ytrain,ypredtrain)
print('Precision:',Precision)

Recall = recall_score(ytrain,ypredtrain)
print('Recall:',Recall)

F1score = f1_score(ytrain,ypredtrain)
print('F1score:',F1score)

Training Accuracy: 0.8892921960072595
Confusion matrix: 
 [[903  21]
 [101  77]]
Precision: 0.7857142857142857
Recall: 0.43258426966292135
F1score: 0.5579710144927537

# Testing data evaluation
ypredtest = log_model.predict(xtest)

Accuracy_log = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_log)

confusionmatrix = confusion_matrix(ytest,ypredtest)
print('Confusion matrix: \n',confusionmatrix)

Precision = precision_score(ytest,ypredtest)
print('Precision:',Precision)

Recall = recall_score(ytest,ypredtest)
print('Recall:',Recall)

F1score = f1_score(ytest,ypredtest)
print('F1score:',F1score)

Testing Accuracy: 0.8831521739130435
Confusion matrix: 
 [[297  12]
 [ 31  28]]
Precision: 0.7
Recall: 0.4745762711864407
F1score: 0.5656565656565656

KNN

from sklearn.neighbors import KNeighborsClassifier

Knn_model = KNeighborsClassifier()
Knn_model.fit(xtrain,ytrain)

KNeighborsClassifier()

# Training data evaluation
ypredtrain = Knn_model.predict(xtrain)

Accuracy_KNN = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_KNN)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix:\n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report:\n',Classification_report)

Training Accuracy: 0.8593466424682396
Confusion_matrix:
 [[915   9]
 [146  32]]
Classification_report:
               precision    recall  f1-score   support

           0       0.86      0.99      0.92       924
           1       0.78      0.18      0.29       178

    accuracy                           0.86      1102
   macro avg       0.82      0.59      0.61      1102
weighted avg       0.85      0.86      0.82      1102

# Testing data evaluation
ypredtest = Knn_model.predict(xtest)
# ypredtest

Accuracy_KNN = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_KNN)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix:\n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report:\n',Classification_report)

Testing Accuracy: 0.8315217391304348
Confusion_matrix:
 [[302   7]
 [ 55   4]]
Classification_report:
               precision    recall  f1-score   support

           0       0.85      0.98      0.91       309
           1       0.36      0.07      0.11        59

    accuracy                           0.83       368
   macro avg       0.60      0.52      0.51       368
weighted avg       0.77      0.83      0.78       368

knn_model = KNeighborsClassifier()
hyp_grid = {'n_neighbors':np.arange(3,40),'p':[1,2]}

gscv_knn_model = GridSearchCV(knn_model,hyp_grid,cv=5)
gscv_knn_model.fit(xtrain,ytrain)
gscv_knn_model.best_estimator_

KNeighborsClassifier(n_neighbors=3, p=1)

Knn_model = KNeighborsClassifier(n_neighbors=7, p=1)
Knn_model.fit(xtrain,ytrain)

KNeighborsClassifier(n_neighbors=7, p=1)

# Training data evaluation
ypredtrain = Knn_model.predict(xtrain)

Accuracy_KNN = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_KNN)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix:\n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report:\n',Classification_report)

Training Accuracy: 0.8647912885662432
Confusion_matrix:
 [[919   5]
 [144  34]]
Classification_report:
               precision    recall  f1-score   support

           0       0.86      0.99      0.93       924
           1       0.87      0.19      0.31       178

    accuracy                           0.86      1102
   macro avg       0.87      0.59      0.62      1102
weighted avg       0.87      0.86      0.83      1102

# Testing data evaluation
ypredtest = Knn_model.predict(xtest)
# ypredtest

Accuracy_KNN = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_KNN)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix:\n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report:\n',Classification_report)

Testing Accuracy: 0.8478260869565217
Confusion_matrix:
 [[306   3]
 [ 53   6]]
Classification_report:
               precision    recall  f1-score   support

           0       0.85      0.99      0.92       309
           1       0.67      0.10      0.18        59

    accuracy                           0.85       368
   macro avg       0.76      0.55      0.55       368
weighted avg       0.82      0.85      0.80       368

Baysian

from sklearn.naive_bayes import BernoulliNB

baysian_model = BernoulliNB()
baysian_model.fit(xtrain,ytrain)

BernoulliNB()

# Training data evaluation
ypredtrain = baysian_model.predict(xtrain)

Accuracy_bay = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_bay)

Training Accuracy: 0.8303085299455535

# Testing data evaluation
ypredtest = baysian_model.predict(xtest)

Accuracy_bay = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_bay)

Testing Accuracy: 0.8478260869565217

Decision Tree

from sklearn.tree import DecisionTreeClassifier, plot_tree

dt_model = DecisionTreeClassifier()
dt_model.fit(xtrain,ytrain)

DecisionTreeClassifier()

# Training data evaluation
ypredtrain = dt_model.predict(xtrain)

Accuracy_DTC = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_DTC)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)

Training Accuracy: 1.0
Confusion_matrix: 
 [[924   0]
 [  0 178]]
Classification_report: 
               precision    recall  f1-score   support

           0       1.00      1.00      1.00       924
           1       1.00      1.00      1.00       178

    accuracy                           1.00      1102
   macro avg       1.00      1.00      1.00      1102
weighted avg       1.00      1.00      1.00      1102

# Testing data evaluation
ypredtest = dt_model.predict(xtest)

Accuracy_DTC = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_DTC)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)

Testing Accuracy: 0.7961956521739131
Confusion_matrix: 
 [[271  38]
 [ 37  22]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.88      0.88      0.88       309
           1       0.37      0.37      0.37        59

    accuracy                           0.80       368
   macro avg       0.62      0.62      0.62       368
weighted avg       0.80      0.80      0.80       368

Hyperparameter tuning

dt_model = DecisionTreeClassifier()

hyp_grid = {'criterion':['gini','entropy'],
            'max_depth': np.arange(3,15),
            'min_samples_split':np.arange(2,20),
            'min_samples_leaf':np.arange(1,15)}

gscv_dt_model = GridSearchCV(dt_model,hyp_grid,cv=5)
gscv_dt_model.fit(xtrain,ytrain)

GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': array([ 3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),
                         'min_samples_leaf': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),
                         'min_samples_split': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
       19])})

gscv_dt_model.best_estimator_

DecisionTreeClassifier(max_depth=3, min_samples_leaf=6)

dt_model = DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=6,min_samples_split=5)
dt_model.fit(xtrain,ytrain)

DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=6,
                       min_samples_split=5)

# Training data evaluation
ypredtrain = dt_model.predict(xtrain)

Accuracy_DTC = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_DTC)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)

Training Accuracy: 0.8584392014519057
Confusion_matrix: 
 [[911  13]
 [143  35]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.86      0.99      0.92       924
           1       0.73      0.20      0.31       178

    accuracy                           0.86      1102
   macro avg       0.80      0.59      0.62      1102
weighted avg       0.84      0.86      0.82      1102

# Testing data evaluation
ypredtest = dt_model.predict(xtest)

Accuracy_DTC = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_DTC)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)

Testing Accuracy: 0.8614130434782609
Confusion_matrix: 
 [[304   5]
 [ 46  13]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.87      0.98      0.92       309
           1       0.72      0.22      0.34        59

    accuracy                           0.86       368
   macro avg       0.80      0.60      0.63       368
weighted avg       0.85      0.86      0.83       368

Plot decision tree after hyperparameter tuning

plt.figure(figsize=(200,150))
tree = plot_tree(dt_model,feature_names=x.columns,class_names=['0','1'],filled=True)
plt.savefig('dt_afterhypt.png')

SVM

from sklearn.svm import SVC

svc_model = SVC()
svc_model.fit(xtrain,ytrain)

SVC()

# Training data evaluation
ypredtrain = svc_model.predict(xtrain)
# ypredtest

Accuracy_SVM = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_SVM)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)

Training Accuracy: 0.911978221415608
Confusion_matrix: 
 [[924   0]
 [ 97  81]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.90      1.00      0.95       924
           1       1.00      0.46      0.63       178

    accuracy                           0.91      1102
   macro avg       0.95      0.73      0.79      1102
weighted avg       0.92      0.91      0.90      1102

# Testing data evaluation
ypredtest = svc_model.predict(xtest)
# ypredtest

Accuracy_SVM = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_SVM)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)

Testing Accuracy: 0.8722826086956522
Confusion_matrix: 
 [[308   1]
 [ 46  13]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.87      1.00      0.93       309
           1       0.93      0.22      0.36        59

    accuracy                           0.87       368
   macro avg       0.90      0.61      0.64       368
weighted avg       0.88      0.87      0.84       368

svc_model = SVC()

hyp_grid = {'C':np.arange(0,50),
            'kernel' : ['linear', 'poly', 'rbf', 'sigmoid']}

gscv_scv_model = GridSearchCV(svc_model,hyp_grid,cv=5)

gscv_scv_model.fit(xtrain,ytrain)

GridSearchCV(cv=5, estimator=SVC(),
             param_grid={'C': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),
                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid']})

gscv_scv_model.best_estimator_

SVC(C=1, kernel='linear')

svc_model = SVC(C=1, kernel='linear')
svc_model.fit(xtrain,ytrain)

SVC(C=1, kernel='linear')

# Training data evaluation
ypredtrain = svc_model.predict(xtrain)
# ypredtest

Accuracy_SVM = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_SVM)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)

Training Accuracy: 0.8920145190562614
Confusion_matrix: 
 [[913  11]
 [108  70]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.89      0.99      0.94       924
           1       0.86      0.39      0.54       178

    accuracy                           0.89      1102
   macro avg       0.88      0.69      0.74      1102
weighted avg       0.89      0.89      0.87      1102

# Testing data evaluation
ypredtest = svc_model.predict(xtest)
# ypredtest

Accuracy_SVM = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_SVM)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)

Testing Accuracy: 0.8967391304347826
Confusion_matrix: 
 [[300   9]
 [ 29  30]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.91      0.97      0.94       309
           1       0.77      0.51      0.61        59

    accuracy                           0.90       368
   macro avg       0.84      0.74      0.78       368
weighted avg       0.89      0.90      0.89       368

ANN

import tensorflow as tf
import keras

from keras import Sequential
from keras.layers import Dense , Dropout

std_scaler = StandardScaler()
xtrain_scaled = std_scaler.fit_transform(xtrain)
xtest_scaled = std_scaler.fit_transform(xtest)

xtrain_scaled

array([[-0.33299714,  2.04892977, -1.55639335, ...,  1.33370758,
         1.19896149,  0.80217198],
       [-0.33299714,  2.04892977, -1.55639335, ..., -0.6096661 ,
        -0.36948053, -0.60635817],
       [ 3.00302877, -0.48805968, -1.55639335, ..., -0.33204129,
        -0.36948053, -0.60635817],
       ...,
       [-0.33299714, -0.48805968,  0.6425111 , ..., -0.6096661 ,
        -0.05579213, -0.60635817],
       [-0.33299714, -0.48805968,  0.6425111 , ..., -1.16491573,
        -0.68316894, -1.16977023],
       [-0.33299714, -0.48805968,  0.6425111 , ..., -0.33204129,
        -0.68316894, -0.04294611]])

xtest_scaled

array([[-0.34921515, -0.46316461,  0.63185405, ...,  0.72604239,
        -0.66802355,  1.09633725],
       [-0.34921515, -0.46316461,  0.63185405, ...,  1.26965094,
         2.04336617,  0.54328433],
       [-0.34921515,  2.15905962, -1.58264396, ..., -0.08937043,
        -0.66802355, -0.56282152],
       ...,
       [-0.34921515, -0.46316461,  0.63185405, ...,  0.72604239,
        -0.36675803,  0.81981079],
       [-0.34921515, -0.46316461,  0.63185405, ..., -0.08937043,
        -0.36675803, -0.56282152],
       [-0.34921515, -0.46316461,  0.63185405, ...,  1.54145521,
        -0.06549251,  1.37286372]])

nn = Sequential()
 # Create input layer and 1st Hidden layer
nn.add(Dense(units = 6,activation='relu',kernel_initializer='he_uniform',input_dim=(58)))  

# Add 1st Dropout layer
nn.add(Dropout(rate=0.5))

# Add 2nd Hidden layer
nn.add(Dense(units = 6,activation='relu',kernel_initializer='he_uniform')) 

# Add 2nd Dropout layer
nn.add(Dropout(rate=0.5))

# Add output layer
nn.add(Dense(units=1 ,activation='sigmoid', kernel_initializer='glorot_uniform'))

# Compile
nn.compile(optimizer='Adam',loss='binary_crossentropy' ,metrics=['accuracy','Recall','Precision'])

# Training
model = nn.fit(xtrain_scaled,ytrain,validation_split=0.3,epochs=200,batch_size=20)

Epoch 1/200
39/39 [==============================] - 1s 11ms/step - loss: 1.1075 - accuracy: 0.5772 - recall: 0.3629 - precision: 0.1541 - val_loss: 0.7224 - val_accuracy: 0.5227 - val_recall: 0.4630 - val_precision: 0.1623
Epoch 2/200
39/39 [==============================] - 0s 3ms/step - loss: 0.9319 - accuracy: 0.6446 - recall: 0.3468 - precision: 0.1822 - val_loss: 0.6553 - val_accuracy: 0.6012 - val_recall: 0.3333 - val_precision: 0.1579
Epoch 3/200
39/39 [==============================] - 0s 3ms/step - loss: 0.8673 - accuracy: 0.6485 - recall: 0.2339 - precision: 0.1415 - val_loss: 0.5992 - val_accuracy: 0.7130 - val_recall: 0.2407 - val_precision: 0.1940
Epoch 4/200
39/39 [==============================] - 0s 3ms/step - loss: 0.7630 - accuracy: 0.7082 - recall: 0.3468 - precision: 0.2299 - val_loss: 0.5626 - val_accuracy: 0.7704 - val_recall: 0.1667 - val_precision: 0.2250
Epoch 5/200
39/39 [==============================] - 0s 3ms/step - loss: 0.7281 - accuracy: 0.7160 - recall: 0.2903 - precision: 0.2156 - val_loss: 0.5348 - val_accuracy: 0.8006 - val_recall: 0.1481 - val_precision: 0.2857
Epoch 6/200
39/39 [==============================] - 0s 3ms/step - loss: 0.6799 - accuracy: 0.7276 - recall: 0.2177 - precision: 0.1929 - val_loss: 0.5162 - val_accuracy: 0.8097 - val_recall: 0.0741 - val_precision: 0.2353
Epoch 7/200
39/39 [==============================] - 0s 2ms/step - loss: 0.5909 - accuracy: 0.7639 - recall: 0.2742 - precision: 0.2698 - val_loss: 0.5023 - val_accuracy: 0.8278 - val_recall: 0.0556 - val_precision: 0.3333
Epoch 8/200
39/39 [==============================] - 0s 2ms/step - loss: 0.6242 - accuracy: 0.7328 - recall: 0.1290 - precision: 0.1404 - val_loss: 0.4918 - val_accuracy: 0.8308 - val_recall: 0.0185 - val_precision: 0.2500
Epoch 9/200
39/39 [==============================] - 0s 3ms/step - loss: 0.5828 - accuracy: 0.7795 - recall: 0.1532 - precision: 0.2262 - val_loss: 0.4836 - val_accuracy: 0.8338 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 10/200
39/39 [==============================] - 0s 3ms/step - loss: 0.5740 - accuracy: 0.7808 - recall: 0.1290 - precision: 0.2078 - val_loss: 0.4778 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 11/200
39/39 [==============================] - 0s 4ms/step - loss: 0.5767 - accuracy: 0.7562 - recall: 0.0403 - precision: 0.0676 - val_loss: 0.4719 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 12/200
39/39 [==============================] - 0s 6ms/step - loss: 0.5269 - accuracy: 0.7847 - recall: 0.0726 - precision: 0.1500 - val_loss: 0.4663 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 13/200
39/39 [==============================] - 0s 6ms/step - loss: 0.5441 - accuracy: 0.7873 - recall: 0.0887 - precision: 0.1774 - val_loss: 0.4636 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 14/200
39/39 [==============================] - 0s 4ms/step - loss: 0.5270 - accuracy: 0.7938 - recall: 0.0645 - precision: 0.1569 - val_loss: 0.4602 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 15/200
39/39 [==============================] - 0s 3ms/step - loss: 0.5227 - accuracy: 0.7938 - recall: 0.0403 - precision: 0.1111 - val_loss: 0.4565 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 16/200
39/39 [==============================] - 0s 3ms/step - loss: 0.5335 - accuracy: 0.8042 - recall: 0.0565 - precision: 0.1707 - val_loss: 0.4530 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 17/200
39/39 [==============================] - 0s 3ms/step - loss: 0.5046 - accuracy: 0.8093 - recall: 0.0484 - precision: 0.1714 - val_loss: 0.4496 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 18/200
39/39 [==============================] - 0s 6ms/step - loss: 0.5372 - accuracy: 0.8132 - recall: 0.0242 - precision: 0.1154 - val_loss: 0.4475 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 19/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4878 - accuracy: 0.8249 - recall: 0.0484 - precision: 0.2609 - val_loss: 0.4456 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 20/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4781 - accuracy: 0.8262 - recall: 0.0484 - precision: 0.2727 - val_loss: 0.4433 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 21/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4869 - accuracy: 0.8236 - recall: 0.0323 - precision: 0.2000 - val_loss: 0.4410 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 22/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4939 - accuracy: 0.8171 - recall: 0.0242 - precision: 0.1304 - val_loss: 0.4395 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 23/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4821 - accuracy: 0.8275 - recall: 0.0081 - precision: 0.0909 - val_loss: 0.4376 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 24/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4705 - accuracy: 0.8353 - recall: 0.0565 - precision: 0.4118 - val_loss: 0.4344 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 25/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4751 - accuracy: 0.8301 - recall: 0.0161 - precision: 0.1818 - val_loss: 0.4323 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 26/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4503 - accuracy: 0.8327 - recall: 0.0323 - precision: 0.3077 - val_loss: 0.4311 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 27/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4499 - accuracy: 0.8327 - recall: 0.0161 - precision: 0.2222 - val_loss: 0.4291 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 28/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4429 - accuracy: 0.8366 - recall: 0.0161 - precision: 0.3333 - val_loss: 0.4281 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 29/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4462 - accuracy: 0.8405 - recall: 0.0403 - precision: 0.5556 - val_loss: 0.4262 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 30/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4584 - accuracy: 0.8405 - recall: 0.0565 - precision: 0.5385 - val_loss: 0.4255 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 31/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4498 - accuracy: 0.8418 - recall: 0.0242 - precision: 0.7500 - val_loss: 0.4238 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 32/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4550 - accuracy: 0.8405 - recall: 0.0161 - precision: 0.6667 - val_loss: 0.4235 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 33/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4276 - accuracy: 0.8431 - recall: 0.0484 - precision: 0.6667 - val_loss: 0.4226 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 34/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4142 - accuracy: 0.8418 - recall: 0.0323 - precision: 0.6667 - val_loss: 0.4196 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 35/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4368 - accuracy: 0.8457 - recall: 0.0565 - precision: 0.7778 - val_loss: 0.4175 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 36/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4422 - accuracy: 0.8431 - recall: 0.0484 - precision: 0.6667 - val_loss: 0.4148 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 37/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4367 - accuracy: 0.8431 - recall: 0.0565 - precision: 0.6364 - val_loss: 0.4140 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 38/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4281 - accuracy: 0.8392 - recall: 0.0242 - precision: 0.5000 - val_loss: 0.4123 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 39/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4324 - accuracy: 0.8457 - recall: 0.0645 - precision: 0.7273 - val_loss: 0.4120 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 40/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4100 - accuracy: 0.8482 - recall: 0.0887 - precision: 0.7333 - val_loss: 0.4104 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 41/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4130 - accuracy: 0.8418 - recall: 0.0484 - precision: 0.6000 - val_loss: 0.4075 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 42/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4193 - accuracy: 0.8457 - recall: 0.0645 - precision: 0.7273 - val_loss: 0.4081 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 43/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4088 - accuracy: 0.8457 - recall: 0.0565 - precision: 0.7778 - val_loss: 0.4081 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 44/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4093 - accuracy: 0.8482 - recall: 0.0887 - precision: 0.7333 - val_loss: 0.4078 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 45/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4141 - accuracy: 0.8470 - recall: 0.0645 - precision: 0.8000 - val_loss: 0.4059 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 46/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4132 - accuracy: 0.8495 - recall: 0.0726 - precision: 0.9000 - val_loss: 0.4064 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 47/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4133 - accuracy: 0.8534 - recall: 0.0968 - precision: 0.9231 - val_loss: 0.4045 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 48/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4055 - accuracy: 0.8521 - recall: 0.0887 - precision: 0.9167 - val_loss: 0.4013 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 49/200
39/39 [==============================] - 0s 4ms/step - loss: 0.4063 - accuracy: 0.8508 - recall: 0.1210 - precision: 0.7143 - val_loss: 0.3990 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 50/200
39/39 [==============================] - 0s 4ms/step - loss: 0.3968 - accuracy: 0.8547 - recall: 0.1048 - precision: 0.9286 - val_loss: 0.3972 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 51/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4007 - accuracy: 0.8534 - recall: 0.1210 - precision: 0.7895 - val_loss: 0.3947 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 52/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3868 - accuracy: 0.8586 - recall: 0.1532 - precision: 0.8261 - val_loss: 0.3932 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 53/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3915 - accuracy: 0.8612 - recall: 0.1613 - precision: 0.8696 - val_loss: 0.3899 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 54/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4005 - accuracy: 0.8547 - recall: 0.1210 - precision: 0.8333 - val_loss: 0.3903 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 55/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4007 - accuracy: 0.8521 - recall: 0.1129 - precision: 0.7778 - val_loss: 0.3876 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 56/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3969 - accuracy: 0.8495 - recall: 0.1129 - precision: 0.7000 - val_loss: 0.3900 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 57/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3953 - accuracy: 0.8495 - recall: 0.1290 - precision: 0.6667 - val_loss: 0.3907 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 58/200
39/39 [==============================] - 0s 3ms/step - loss: 0.4006 - accuracy: 0.8573 - recall: 0.1694 - precision: 0.7500 - val_loss: 0.3870 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 59/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3869 - accuracy: 0.8508 - recall: 0.1210 - precision: 0.7143 - val_loss: 0.3869 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 60/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3982 - accuracy: 0.8482 - recall: 0.1129 - precision: 0.6667 - val_loss: 0.3870 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 61/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3853 - accuracy: 0.8599 - recall: 0.1371 - precision: 0.9444 - val_loss: 0.3850 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 62/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3730 - accuracy: 0.8651 - recall: 0.1694 - precision: 0.9545 - val_loss: 0.3804 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 63/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3722 - accuracy: 0.8638 - recall: 0.1855 - precision: 0.8519 - val_loss: 0.3786 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 64/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3921 - accuracy: 0.8534 - recall: 0.1371 - precision: 0.7391 - val_loss: 0.3807 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 65/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3854 - accuracy: 0.8534 - recall: 0.1371 - precision: 0.7391 - val_loss: 0.3806 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 66/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3922 - accuracy: 0.8508 - recall: 0.1210 - precision: 0.7143 - val_loss: 0.3807 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 67/200
39/39 [==============================] - 0s 6ms/step - loss: 0.3642 - accuracy: 0.8625 - recall: 0.1935 - precision: 0.8000 - val_loss: 0.3798 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 68/200
39/39 [==============================] - 0s 6ms/step - loss: 0.3645 - accuracy: 0.8586 - recall: 0.1532 - precision: 0.8261 - val_loss: 0.3835 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 69/200
39/39 [==============================] - 0s 4ms/step - loss: 0.3754 - accuracy: 0.8495 - recall: 0.1048 - precision: 0.7222 - val_loss: 0.3848 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 70/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3921 - accuracy: 0.8560 - recall: 0.1613 - precision: 0.7407 - val_loss: 0.3828 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 71/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3836 - accuracy: 0.8560 - recall: 0.1694 - precision: 0.7241 - val_loss: 0.3817 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 72/200
39/39 [==============================] - 0s 4ms/step - loss: 0.3719 - accuracy: 0.8547 - recall: 0.1210 - precision: 0.8333 - val_loss: 0.3813 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 73/200
39/39 [==============================] - 0s 4ms/step - loss: 0.3679 - accuracy: 0.8690 - recall: 0.2339 - precision: 0.8286 - val_loss: 0.3818 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 74/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3696 - accuracy: 0.8677 - recall: 0.2016 - precision: 0.8929 - val_loss: 0.3781 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 75/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3785 - accuracy: 0.8521 - recall: 0.1371 - precision: 0.7083 - val_loss: 0.3808 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 76/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3670 - accuracy: 0.8638 - recall: 0.1855 - precision: 0.8519 - val_loss: 0.3803 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 77/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3632 - accuracy: 0.8573 - recall: 0.1774 - precision: 0.7333 - val_loss: 0.3781 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 78/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.8625 - recall: 0.1774 - precision: 0.8462 - val_loss: 0.3792 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 79/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3465 - accuracy: 0.8703 - recall: 0.2419 - precision: 0.8333 - val_loss: 0.3799 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 80/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3537 - accuracy: 0.8664 - recall: 0.2258 - precision: 0.8000 - val_loss: 0.3774 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 81/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3758 - accuracy: 0.8521 - recall: 0.1290 - precision: 0.7273 - val_loss: 0.3794 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 82/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3665 - accuracy: 0.8573 - recall: 0.1694 - precision: 0.7500 - val_loss: 0.3809 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 83/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3677 - accuracy: 0.8560 - recall: 0.1532 - precision: 0.7600 - val_loss: 0.3816 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 84/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3768 - accuracy: 0.8547 - recall: 0.1371 - precision: 0.7727 - val_loss: 0.3792 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 85/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3646 - accuracy: 0.8625 - recall: 0.1855 - precision: 0.8214 - val_loss: 0.3806 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 86/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3880 - accuracy: 0.8508 - recall: 0.1371 - precision: 0.6800 - val_loss: 0.3818 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 87/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3679 - accuracy: 0.8573 - recall: 0.1452 - precision: 0.8182 - val_loss: 0.3827 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 88/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3591 - accuracy: 0.8664 - recall: 0.2097 - precision: 0.8387 - val_loss: 0.3794 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 89/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3605 - accuracy: 0.8638 - recall: 0.2016 - precision: 0.8065 - val_loss: 0.3819 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 90/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3709 - accuracy: 0.8547 - recall: 0.1532 - precision: 0.7308 - val_loss: 0.3823 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 91/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3785 - accuracy: 0.8534 - recall: 0.1210 - precision: 0.7895 - val_loss: 0.3860 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 92/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3462 - accuracy: 0.8625 - recall: 0.1855 - precision: 0.8214 - val_loss: 0.3837 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 93/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3717 - accuracy: 0.8586 - recall: 0.1694 - precision: 0.7778 - val_loss: 0.3878 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 94/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3585 - accuracy: 0.8534 - recall: 0.1371 - precision: 0.7391 - val_loss: 0.3841 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 95/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3520 - accuracy: 0.8651 - recall: 0.1935 - precision: 0.8571 - val_loss: 0.3830 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 96/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3487 - accuracy: 0.8703 - recall: 0.2581 - precision: 0.8000 - val_loss: 0.3829 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 97/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3449 - accuracy: 0.8729 - recall: 0.2419 - precision: 0.8824 - val_loss: 0.3833 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 98/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3646 - accuracy: 0.8495 - recall: 0.1210 - precision: 0.6818 - val_loss: 0.3835 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 99/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3510 - accuracy: 0.8664 - recall: 0.2177 - precision: 0.8182 - val_loss: 0.3871 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 100/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3433 - accuracy: 0.8651 - recall: 0.2177 - precision: 0.7941 - val_loss: 0.3896 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 101/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3335 - accuracy: 0.8729 - recall: 0.2419 - precision: 0.8824 - val_loss: 0.3917 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 102/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3405 - accuracy: 0.8651 - recall: 0.2258 - precision: 0.7778 - val_loss: 0.3924 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 103/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3465 - accuracy: 0.8690 - recall: 0.2661 - precision: 0.7674 - val_loss: 0.3983 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 104/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3384 - accuracy: 0.8651 - recall: 0.1694 - precision: 0.9545 - val_loss: 0.3981 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 105/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3527 - accuracy: 0.8664 - recall: 0.2258 - precision: 0.8000 - val_loss: 0.3966 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 106/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3452 - accuracy: 0.8638 - recall: 0.1935 - precision: 0.8276 - val_loss: 0.3975 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 107/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3595 - accuracy: 0.8612 - recall: 0.2016 - precision: 0.7576 - val_loss: 0.3954 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 108/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3328 - accuracy: 0.8690 - recall: 0.2177 - precision: 0.8710 - val_loss: 0.3970 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 109/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3401 - accuracy: 0.8638 - recall: 0.2097 - precision: 0.7879 - val_loss: 0.3985 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 110/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3551 - accuracy: 0.8521 - recall: 0.1371 - precision: 0.7083 - val_loss: 0.4004 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 111/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3634 - accuracy: 0.8508 - recall: 0.1371 - precision: 0.6800 - val_loss: 0.4027 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 112/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3423 - accuracy: 0.8690 - recall: 0.2177 - precision: 0.8710 - val_loss: 0.4051 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 113/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3362 - accuracy: 0.8677 - recall: 0.2097 - precision: 0.8667 - val_loss: 0.4027 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 114/200
39/39 [==============================] - 0s 6ms/step - loss: 0.3517 - accuracy: 0.8547 - recall: 0.1774 - precision: 0.6875 - val_loss: 0.4019 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 115/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3578 - accuracy: 0.8482 - recall: 0.1210 - precision: 0.6522 - val_loss: 0.4091 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 116/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3454 - accuracy: 0.8560 - recall: 0.1532 - precision: 0.7600 - val_loss: 0.4119 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 117/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3566 - accuracy: 0.8547 - recall: 0.1371 - precision: 0.7727 - val_loss: 0.4137 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 118/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3433 - accuracy: 0.8612 - recall: 0.1452 - precision: 0.9474 - val_loss: 0.4096 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 119/200
39/39 [==============================] - 0s 4ms/step - loss: 0.3295 - accuracy: 0.8677 - recall: 0.1935 - precision: 0.9231 - val_loss: 0.4042 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 120/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3476 - accuracy: 0.8534 - recall: 0.1452 - precision: 0.7200 - val_loss: 0.4075 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 121/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3312 - accuracy: 0.8664 - recall: 0.2419 - precision: 0.7692 - val_loss: 0.4084 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 122/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3502 - accuracy: 0.8586 - recall: 0.1694 - precision: 0.7778 - val_loss: 0.4142 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 123/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3274 - accuracy: 0.8664 - recall: 0.1935 - precision: 0.8889 - val_loss: 0.4172 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 124/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3334 - accuracy: 0.8612 - recall: 0.1774 - precision: 0.8148 - val_loss: 0.4141 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 125/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3184 - accuracy: 0.8742 - recall: 0.2419 - precision: 0.9091 - val_loss: 0.4135 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 126/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3212 - accuracy: 0.8703 - recall: 0.2581 - precision: 0.8000 - val_loss: 0.4122 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 127/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3188 - accuracy: 0.8703 - recall: 0.2823 - precision: 0.7609 - val_loss: 0.4163 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 128/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3223 - accuracy: 0.8651 - recall: 0.2500 - precision: 0.7381 - val_loss: 0.4165 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 129/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3336 - accuracy: 0.8573 - recall: 0.2016 - precision: 0.6944 - val_loss: 0.4162 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 130/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3129 - accuracy: 0.8703 - recall: 0.2581 - precision: 0.8000 - val_loss: 0.4226 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 131/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3237 - accuracy: 0.8677 - recall: 0.2258 - precision: 0.8235 - val_loss: 0.4217 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 132/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3480 - accuracy: 0.8508 - recall: 0.1532 - precision: 0.6552 - val_loss: 0.4220 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 133/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3375 - accuracy: 0.8651 - recall: 0.2097 - precision: 0.8125 - val_loss: 0.4237 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 134/200
39/39 [==============================] - 0s 4ms/step - loss: 0.3297 - accuracy: 0.8586 - recall: 0.2177 - precision: 0.6923 - val_loss: 0.4296 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 135/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3267 - accuracy: 0.8625 - recall: 0.1935 - precision: 0.8000 - val_loss: 0.4300 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 136/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3313 - accuracy: 0.8638 - recall: 0.1935 - precision: 0.8276 - val_loss: 0.4323 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 137/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3338 - accuracy: 0.8586 - recall: 0.2177 - precision: 0.6923 - val_loss: 0.4383 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 138/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3330 - accuracy: 0.8625 - recall: 0.1935 - precision: 0.8000 - val_loss: 0.4430 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 139/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3210 - accuracy: 0.8638 - recall: 0.1855 - precision: 0.8519 - val_loss: 0.4434 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 140/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3239 - accuracy: 0.8794 - recall: 0.2823 - precision: 0.8974 - val_loss: 0.4374 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 141/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3205 - accuracy: 0.8664 - recall: 0.2339 - precision: 0.7838 - val_loss: 0.4379 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 142/200
39/39 [==============================] - 0s 4ms/step - loss: 0.3159 - accuracy: 0.8677 - recall: 0.2258 - precision: 0.8235 - val_loss: 0.4360 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 143/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3269 - accuracy: 0.8586 - recall: 0.2177 - precision: 0.6923 - val_loss: 0.4388 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 144/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3175 - accuracy: 0.8664 - recall: 0.2500 - precision: 0.7561 - val_loss: 0.4452 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 145/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3147 - accuracy: 0.8742 - recall: 0.2823 - precision: 0.8140 - val_loss: 0.4441 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 146/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3114 - accuracy: 0.8703 - recall: 0.2742 - precision: 0.7727 - val_loss: 0.4483 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 147/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3372 - accuracy: 0.8651 - recall: 0.2339 - precision: 0.7632 - val_loss: 0.4505 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 148/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3239 - accuracy: 0.8729 - recall: 0.2661 - precision: 0.8250 - val_loss: 0.4476 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 149/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3114 - accuracy: 0.8742 - recall: 0.2823 - precision: 0.8140 - val_loss: 0.4469 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 150/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3414 - accuracy: 0.8625 - recall: 0.2500 - precision: 0.7045 - val_loss: 0.4513 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 151/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3101 - accuracy: 0.8768 - recall: 0.2661 - precision: 0.8919 - val_loss: 0.4552 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000
Epoch 152/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3015 - accuracy: 0.8820 - recall: 0.3145 - precision: 0.8667 - val_loss: 0.4613 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 153/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3156 - accuracy: 0.8716 - recall: 0.2661 - precision: 0.8049 - val_loss: 0.4634 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 154/200
39/39 [==============================] - 0s 6ms/step - loss: 0.2951 - accuracy: 0.8833 - recall: 0.3306 - precision: 0.8542 - val_loss: 0.4636 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 155/200
39/39 [==============================] - 0s 6ms/step - loss: 0.2962 - accuracy: 0.8768 - recall: 0.3065 - precision: 0.8085 - val_loss: 0.4640 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 156/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3220 - accuracy: 0.8651 - recall: 0.2339 - precision: 0.7632 - val_loss: 0.4640 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 157/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3086 - accuracy: 0.8742 - recall: 0.3226 - precision: 0.7547 - val_loss: 0.4612 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 158/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3269 - accuracy: 0.8638 - recall: 0.2419 - precision: 0.7317 - val_loss: 0.4602 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 159/200
39/39 [==============================] - 0s 6ms/step - loss: 0.3181 - accuracy: 0.8690 - recall: 0.2661 - precision: 0.7674 - val_loss: 0.4667 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 160/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3121 - accuracy: 0.8755 - recall: 0.2823 - precision: 0.8333 - val_loss: 0.4711 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 161/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3154 - accuracy: 0.8690 - recall: 0.2661 - precision: 0.7674 - val_loss: 0.4733 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 162/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3265 - accuracy: 0.8599 - recall: 0.2016 - precision: 0.7353 - val_loss: 0.4753 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000
Epoch 163/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3121 - accuracy: 0.8703 - recall: 0.2581 - precision: 0.8000 - val_loss: 0.4791 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 164/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3046 - accuracy: 0.8742 - recall: 0.2823 - precision: 0.8140 - val_loss: 0.4811 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 165/200
39/39 [==============================] - 0s 3ms/step - loss: 0.2920 - accuracy: 0.8820 - recall: 0.3306 - precision: 0.8367 - val_loss: 0.4778 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 166/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3201 - accuracy: 0.8664 - recall: 0.2177 - precision: 0.8182 - val_loss: 0.4785 - val_accuracy: 0.8550 - val_recall: 0.1111 - val_precision: 1.0000
Epoch 167/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3040 - accuracy: 0.8794 - recall: 0.3548 - precision: 0.7719 - val_loss: 0.4817 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 168/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3206 - accuracy: 0.8651 - recall: 0.2500 - precision: 0.7381 - val_loss: 0.4809 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 169/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3282 - accuracy: 0.8612 - recall: 0.2339 - precision: 0.7073 - val_loss: 0.4832 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 170/200
39/39 [==============================] - 0s 5ms/step - loss: 0.3162 - accuracy: 0.8664 - recall: 0.2177 - precision: 0.8182 - val_loss: 0.4828 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000
Epoch 171/200
39/39 [==============================] - 0s 6ms/step - loss: 0.3176 - accuracy: 0.8612 - recall: 0.2177 - precision: 0.7297 - val_loss: 0.4865 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 172/200
39/39 [==============================] - 0s 6ms/step - loss: 0.3090 - accuracy: 0.8690 - recall: 0.2661 - precision: 0.7674 - val_loss: 0.4876 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 173/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3183 - accuracy: 0.8625 - recall: 0.2500 - precision: 0.7045 - val_loss: 0.4984 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 174/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3024 - accuracy: 0.8755 - recall: 0.2742 - precision: 0.8500 - val_loss: 0.5042 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 175/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3090 - accuracy: 0.8755 - recall: 0.2661 - precision: 0.8684 - val_loss: 0.5064 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 176/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3112 - accuracy: 0.8625 - recall: 0.2097 - precision: 0.7647 - val_loss: 0.5059 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 177/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3163 - accuracy: 0.8729 - recall: 0.2500 - precision: 0.8611 - val_loss: 0.5090 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 178/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3091 - accuracy: 0.8716 - recall: 0.2339 - precision: 0.8788 - val_loss: 0.5161 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 179/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3009 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5185 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 180/200
39/39 [==============================] - 0s 3ms/step - loss: 0.2993 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5108 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 181/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3158 - accuracy: 0.8703 - recall: 0.2823 - precision: 0.7609 - val_loss: 0.5172 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 182/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3082 - accuracy: 0.8755 - recall: 0.2742 - precision: 0.8500 - val_loss: 0.5207 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 183/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3106 - accuracy: 0.8690 - recall: 0.2581 - precision: 0.7805 - val_loss: 0.5184 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 184/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3051 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5218 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 185/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3113 - accuracy: 0.8690 - recall: 0.2419 - precision: 0.8108 - val_loss: 0.5248 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 186/200
39/39 [==============================] - 0s 2ms/step - loss: 0.3175 - accuracy: 0.8651 - recall: 0.2339 - precision: 0.7632 - val_loss: 0.5180 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 187/200
39/39 [==============================] - 0s 2ms/step - loss: 0.2984 - accuracy: 0.8690 - recall: 0.2419 - precision: 0.8108 - val_loss: 0.5206 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 188/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3052 - accuracy: 0.8690 - recall: 0.2984 - precision: 0.7255 - val_loss: 0.5214 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 189/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3000 - accuracy: 0.8833 - recall: 0.3306 - precision: 0.8542 - val_loss: 0.5295 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 190/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3024 - accuracy: 0.8677 - recall: 0.2500 - precision: 0.7750 - val_loss: 0.5297 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 191/200
39/39 [==============================] - 0s 3ms/step - loss: 0.2897 - accuracy: 0.8833 - recall: 0.3468 - precision: 0.8269 - val_loss: 0.5285 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 192/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3104 - accuracy: 0.8625 - recall: 0.2419 - precision: 0.7143 - val_loss: 0.5329 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 193/200
39/39 [==============================] - 0s 3ms/step - loss: 0.2969 - accuracy: 0.8716 - recall: 0.2742 - precision: 0.7907 - val_loss: 0.5362 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 194/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3191 - accuracy: 0.8547 - recall: 0.1935 - precision: 0.6667 - val_loss: 0.5417 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 195/200
39/39 [==============================] - 0s 3ms/step - loss: 0.2948 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5458 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 196/200
39/39 [==============================] - 0s 3ms/step - loss: 0.2989 - accuracy: 0.8703 - recall: 0.2500 - precision: 0.8158 - val_loss: 0.5518 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 197/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3147 - accuracy: 0.8703 - recall: 0.2823 - precision: 0.7609 - val_loss: 0.5514 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 198/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3016 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5512 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000
Epoch 199/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3047 - accuracy: 0.8794 - recall: 0.3306 - precision: 0.8039 - val_loss: 0.5541 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
Epoch 200/200
39/39 [==============================] - 0s 3ms/step - loss: 0.3025 - accuracy: 0.8755 - recall: 0.2500 - precision: 0.9118 - val_loss: 0.5626 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000

ypredtest = nn.predict(xtest_scaled)

12/12 [==============================] - 0s 1ms/step

ypredtest

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       [2.26473090e-10]], dtype=float32)

ypredtest = list(map(int,(ypredtest>0.5)))

print(ypredtest)

[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Accuracy = accuracy_score(ytest,ypredtest)
Accuracy

0.8478260869565217

model.history

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model.history.keys()

dict_keys(['loss', 'accuracy', 'recall', 'precision', 'val_loss', 'val_accuracy', 'val_recall', 'val_precision'])

# Visualizing accuracy
plt.plot(model.history['accuracy'])
plt.plot(model.history['val_accuracy'])
plt.legend(['training_accuracy','testing_accuracy'])

<matplotlib.legend.Legend at 0x2ea8d930b80>

# Visualizing loss
plt.plot(model.history['loss'])
plt.plot(model.history['val_loss'])
plt.legend(['training_loss','testing_loss'])

<matplotlib.legend.Legend at 0x2ea8d9f9190>

Final Predictions

Logistic regression model gives high accuracy, so selected as best model.

Predictions = log_model.predict(xtest)
Predictions

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pd.options.display.max_rows=368

final_output = pd.DataFrame(Predictions,columns=['Final_Predictions'])
final_output

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9) Clustering - Find intersting clusters using K-means, Heirarchical and DBSCAN clustering. Connect to Domain scenario and its usefulness in analysis (Ignore Attrition Column)

a) Grouping of Emploees

from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.cluster import AgglomerativeClustering
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.metrics import silhouette_score
import scipy.cluster.hierarchy as shc

x

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227

1470 rows × 58 columns

Reducing the dimensionality of the Data

pca = PCA(n_components = 2)
X_principal = pca.fit_transform(x)
X_principal = pd.DataFrame(X_principal)
X_principal.columns = ['P1', 'P2']

X_principal

	P1	P2
0	-0.123227	1.753045
1	0.708353	-1.417164
2	-2.489738	1.244741
3	-0.952054	-0.341545
4	-1.946408	0.797334
...	...	...
1465	-0.837692	0.375050
1466	0.762103	-0.371828
1467	-1.293807	-0.939619
1468	1.156983	0.414920
1469	-1.291327	0.138558

1470 rows × 2 columns

K-Means Clustering

kmeans_model = KMeans(n_clusters=3)  # n_clusters=8  >> default
kmeans_model.fit(X_principal)

KMeans(n_clusters=3)

kmeans_model.inertia_

3686.284119036042

y_var = kmeans_model.fit_predict(X_principal)
y_var

array([1, 2, 1, ..., 1, 2, 1])

index_0 = np.where(y_var==0)
index_0

(array([  18,   25,   28,   29,   45,   62,   63,   65,   85,   90,   93,
          95,   98,  105,  106,  110,  112,  119,  123,  126,  147,  158,
         165,  178,  186,  187,  189,  190,  194,  213,  218,  231,  233,
         235,  237,  244,  257,  263,  268,  270,  271,  279,  280,  290,
         295,  300,  311,  314,  316,  326,  329,  367,  375,  379,  390,
         392,  400,  401,  406,  408,  411,  417,  424,  425,  427,  429,
         445,  448,  465,  466,  473,  477,  489,  492,  497,  502,  510,
         533,  534,  535,  538,  544,  552,  561,  568,  584,  588,  592,
         595,  609,  616,  624,  625,  627,  646,  649,  653,  674,  677,
         695,  699,  701,  714,  716,  721,  723,  728,  736,  738,  741,
         743,  745,  746,  749,  750,  755,  758,  760,  766,  770,  771,
         774,  787,  789,  799,  804,  806,  810,  812,  813,  814,  821,
         837,  838,  851,  858,  861,  867,  869,  890,  894,  898,  899,
         904,  907,  913,  914,  916,  918,  919,  922,  926,  936,  937,
         945,  947,  954,  955,  956,  962,  966,  971,  975,  976,  994,
         999, 1008, 1009, 1010, 1024, 1031, 1034, 1043, 1054, 1055, 1076,
        1078, 1080, 1086, 1093, 1096, 1103, 1111, 1116, 1126, 1129, 1135,
        1138, 1140, 1154, 1156, 1162, 1166, 1176, 1177, 1181, 1184, 1185,
        1194, 1195, 1221, 1223, 1225, 1242, 1264, 1268, 1275, 1277, 1278,
        1295, 1301, 1303, 1310, 1327, 1330, 1331, 1348, 1351, 1357, 1374,
        1377, 1401, 1403, 1414, 1430, 1437, 1443, 1445], dtype=int64),)

cluster_0 = x.iloc[index_0]
cluster_0

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
18	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.760373	1.032581	-0.887515	1.061787	0.0	-1.664727	-1.575686	0.595834	-1.026167	1.749610	1.153254	1.896174	1.083275	-0.277594	0.216054	-0.426230	0.266233	0.0	-0.932014	2.535365	0.155707	0.338096	2.937711	1.041095	0.252146	0.806541
25	0	0	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.760373	1.188765	-0.517332	0.085049	0.0	-1.649772	0.254625	-0.388296	0.379672	2.653309	0.246200	2.675333	-0.502870	0.523316	-1.150554	-0.426230	1.191438	0.0	0.241988	1.892537	0.155707	-1.077862	1.141629	2.421585	0.562576	1.086895
28	0	0	1	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.774856	-0.806912	-0.270544	1.061787	0.0	-1.643126	-1.575686	-1.175601	-1.026167	0.845911	1.153254	0.795747	-1.717284	0.122861	-0.330589	-0.426230	1.191438	0.0	0.241988	1.635406	0.931603	0.338096	2.447870	0.488900	0.873006	3.610079
29	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0.993860	-0.241677	-0.887515	1.061787	0.0	-1.639803	-0.660531	0.841867	0.379672	2.653309	-1.567907	2.644099	1.195848	0.122861	-0.877232	-0.426230	1.191438	0.0	-0.932014	1.378275	-0.620189	-1.077862	-0.817734	-0.615492	-0.058285	-0.875581
45	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	0.446350	1.382135	0.346427	0.085049	0.0	-1.606570	-0.660531	-0.831155	0.379672	2.653309	0.246200	2.771161	0.276429	-0.678049	-0.877232	-0.426230	1.191438	0.0	-0.932014	1.506840	-2.171982	0.338096	2.447870	2.973780	3.977310	1.086895
62	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	1	0	0	1	1.431867	0.462388	-0.270544	-0.891688	0.0	-1.570014	-0.660531	-1.126394	-1.026167	2.653309	0.246200	2.600116	0.335597	0.923771	-0.877232	-0.426230	1.191438	0.0	0.241988	2.278234	-0.620189	-1.077862	3.264271	-0.339394	3.356449	1.086895
63	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	1	2.417384	1.568068	1.950552	0.085049	0.0	-1.568353	-1.575686	1.629171	0.379672	0.845911	-1.567907	0.240965	-1.680744	1.724681	-1.150554	-0.426230	1.191438	0.0	-0.932014	2.149668	0.155707	-1.077862	2.284590	3.249878	1.493867	1.367249
65	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	1	0	0	1	1.979376	0.083085	-0.147150	0.085049	0.0	-1.563368	1.169781	-1.618459	0.379672	1.749610	0.246200	1.753601	0.761296	-0.277594	-0.330589	-0.426230	0.266233	0.0	2.589994	1.249709	-0.620189	0.338096	-0.327893	-1.167687	-0.679146	-0.595227
85	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	2.088878	1.481299	-0.270544	0.085049	0.0	-1.516842	1.169781	-0.831155	-2.432006	0.845911	1.153254	0.160861	1.037880	0.523316	-1.150554	-0.426230	-1.584178	0.0	-0.932014	3.306758	0.155707	-1.077862	-0.164613	-0.063296	-0.679146	-0.595227
90	0	1	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.336849	-0.675520	-1.010909	1.061787	0.0	-1.505211	0.254625	0.595834	-1.026167	1.749610	-0.660853	1.487365	-0.027842	-0.678049	1.855984	2.346151	1.191438	0.0	0.241988	1.378275	0.155707	-1.077862	2.447870	-0.339394	2.735589	1.927956
93	0	1	0	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	0.993860	-0.407777	-1.010909	0.085049	0.0	-1.496903	0.254625	-1.274014	-1.026167	0.845911	-1.567907	0.886051	-1.569997	-0.277594	-0.603911	-0.426230	0.266233	0.0	0.241988	1.249709	1.707500	-1.077862	0.488508	1.317193	2.114728	0.245834
95	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.869874	1.027623	-0.887515	1.061787	0.0	-1.493579	-1.575686	-0.289883	0.379672	0.845911	0.246200	1.497139	1.361546	2.525591	-0.877232	-0.426230	-1.584178	0.0	0.241988	0.606882	1.707500	-2.493820	-0.491174	-0.339394	-0.679146	-0.314873
98	0	0	1	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	1	2.307882	-0.298696	0.099639	1.061787	0.0	-1.485271	1.169781	-1.421633	0.379672	1.749610	0.246200	1.565770	1.418887	-1.078504	-0.603911	-0.426230	0.266233	0.0	-0.932014	3.435323	-1.396086	-1.077862	4.897073	1.593291	-0.368715	1.086895
105	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	2.417384	1.530881	-0.887515	1.061787	0.0	-1.470317	0.254625	-1.667666	-1.026167	2.653309	1.153254	2.622214	1.069361	2.525591	1.582663	2.346151	1.191438	0.0	0.241988	2.406799	0.155707	0.338096	-0.654454	-0.615492	-0.058285	-0.595227
106	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	1	1.431867	0.774755	-1.010909	0.085049	0.0	-1.468655	-1.575686	0.349801	0.379672	2.653309	-0.660853	2.479428	-0.640600	0.122861	1.036019	-0.426230	-1.584178	0.0	-0.932014	2.149668	-1.396086	-1.077862	0.161947	-0.339394	-0.679146	0.806541
110	0	1	0	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.541369	1.620129	-1.010909	1.061787	0.0	-1.462009	-1.575686	-1.766079	-1.026167	0.845911	-1.567907	0.208456	1.613817	0.122861	1.309341	2.346151	0.266233	0.0	-0.932014	1.506840	-1.396086	-1.077862	0.978348	2.145487	3.046019	1.086895
112	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	1	0	0	1	1.869874	-1.637412	2.073946	0.085049	0.0	-1.457024	1.169781	-1.766079	1.785511	1.749610	1.153254	2.300096	-0.062134	-0.277594	-0.877232	-0.426230	0.266233	0.0	-0.932014	1.506840	0.155707	0.338096	-0.327893	-0.339394	0.562576	-0.034520
119	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	0.665354	-1.012678	2.073946	-0.891688	0.0	-1.440407	0.254625	1.284725	0.379672	1.749610	1.153254	2.221691	0.728128	-0.678049	-0.877232	-0.426230	1.191438	0.0	1.415991	1.763972	0.155707	1.754054	2.937711	2.145487	0.562576	2.208310
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937	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0.227347	-0.968054	0.469821	1.061787	0.0	0.468803	0.254625	1.383138	-1.026167	1.749610	-0.660853	2.256538	0.424560	1.324226	-0.603911	-0.426230	1.191438	0.0	1.415991	1.249709	0.931603	0.338096	1.958030	1.317193	3.977310	-0.595227
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954	1	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	0.555852	-0.762289	-0.887515	-1.868426	0.0	0.513667	0.254625	-1.421633	0.379672	1.749610	0.246200	2.413347	1.724282	-1.078504	-0.603911	-0.426230	1.191438	0.0	-0.932014	1.249709	0.155707	-1.077862	2.121310	1.041095	-0.058285	1.647603
955	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.979376	-1.290337	-0.887515	-0.891688	0.0	0.516990	1.169781	-0.388296	-2.432006	2.653309	0.246200	2.695094	-1.028915	0.523316	-0.330589	-0.426230	1.191438	0.0	0.241988	1.506840	1.707500	0.338096	1.958030	1.317193	2.114728	1.927956
956	0	0	1	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	2.088878	-1.478749	-0.147150	1.061787	0.0	0.520313	1.169781	1.629171	0.379672	2.653309	-0.660853	2.807708	-1.446321	1.324226	-0.330589	-0.426230	-1.584178	0.0	-0.932014	3.178192	0.931603	0.338096	-0.001333	-0.339394	1.493867	0.806541
962	0	0	1	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	0	1	0	0	1	1.541369	-0.080535	-0.517332	0.085049	0.0	0.543576	0.254625	0.891073	0.379672	1.749610	-0.660853	1.598491	0.460257	-0.678049	-1.150554	-0.426230	-0.658973	0.0	0.241988	2.792496	-0.620189	0.338096	4.243953	1.317193	-0.679146	1.647603
966	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	0	0	1	2.307882	-0.499504	-0.270544	1.061787	0.0	0.556869	0.254625	-0.634329	-1.026167	0.845911	-1.567907	0.744752	-0.321853	1.724681	-0.330589	-0.426230	1.191438	0.0	-0.932014	2.535365	-2.171982	-1.077862	0.488508	1.317193	0.873006	1.367249
971	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	1.541369	1.493695	0.223033	-0.891688	0.0	0.568500	1.169781	0.792660	-1.026167	1.749610	-0.660853	1.410660	1.423103	0.122861	0.216054	-0.426230	-0.658973	0.0	-0.932014	2.278234	-1.396086	-1.077862	-0.327893	-0.615492	-0.679146	-0.314873
975	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	1.979376	-1.327524	0.469821	1.061787	0.0	0.576809	-1.575686	0.940280	1.785511	1.749610	0.246200	1.528161	-0.707779	1.324226	0.489376	-0.426230	0.266233	0.0	-0.932014	1.635406	-0.620189	-1.077862	1.958030	0.764998	0.252146	1.086895
976	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	0	0	1	2.088878	1.404447	1.703764	0.085049	0.0	0.578470	1.169781	0.103769	0.379672	1.749610	-0.660853	1.465905	0.551187	0.523316	-0.877232	-0.426230	-1.584178	0.0	0.241988	2.792496	-2.171982	0.338096	1.958030	3.249878	3.977310	1.367249
994	0	1	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	0	0	1	1.650871	-1.191173	2.320735	-0.891688	0.0	0.624996	1.169781	-0.339090	1.785511	1.749610	0.246200	1.432971	-0.643973	-0.277594	-1.150554	-0.426230	-0.658973	0.0	0.241988	1.635406	0.155707	-1.077862	-0.327893	-0.339394	-0.679146	-0.595227
999	0	0	1	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.555852	0.854086	0.099639	0.085049	0.0	0.636627	0.254625	-1.716872	0.379672	1.749610	-1.567907	2.187695	0.323651	-1.078504	-0.330589	-0.426230	0.266233	0.0	0.241988	1.249709	1.707500	0.338096	2.121310	0.764998	-0.679146	1.367249
1008	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	1.869874	0.417764	-1.010909	0.085049	0.0	0.659890	1.169781	-0.585122	0.379672	1.749610	1.153254	2.300096	-1.217240	1.324226	1.036019	-0.426230	1.191438	0.0	-0.932014	2.278234	0.155707	-1.077862	2.121310	0.764998	3.046019	0.806541
1009	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	1	2.307882	0.626009	-1.010909	0.085049	0.0	0.661552	1.169781	0.497421	0.379672	2.653309	-1.567907	2.804308	1.144410	0.122861	1.582663	2.346151	0.266233	0.0	0.241988	2.663930	0.155707	0.338096	0.325228	1.041095	-0.368715	0.245834
1010	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	0	0	1	1.979376	0.826816	-1.010909	1.061787	0.0	0.663213	-0.660531	0.743454	1.785511	1.749610	1.153254	1.748501	-0.266902	-0.277594	-0.603911	-0.426230	1.191438	0.0	1.415991	2.535365	0.931603	1.754054	-0.001333	0.764998	-0.679146	-1.155935
1024	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.103362	-1.099446	-0.887515	1.061787	0.0	0.694784	-1.575686	0.792660	0.379672	1.749610	0.246200	2.266312	1.741288	0.122861	1.036019	-0.426230	-0.658973	0.0	1.415991	1.892537	-0.620189	1.754054	2.121310	3.525976	0.873006	0.526188
1031	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	1	0.993860	-1.054823	-0.023755	0.085049	0.0	0.718047	-1.575686	-0.683535	0.379672	0.845911	1.153254	0.763450	0.235110	0.523316	-1.150554	-0.426230	-1.584178	0.0	0.241988	2.149668	-1.396086	1.754054	-0.001333	0.764998	0.562576	-0.314873
1034	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	1	0.884358	0.583864	1.333581	0.085049	0.0	0.723032	-0.660531	1.432345	-2.432006	0.845911	-1.567907	0.923872	0.779988	-0.277594	0.762698	-0.426230	-0.658973	0.0	0.241988	1.635406	-0.620189	0.338096	-0.001333	0.764998	-0.679146	0.806541
1043	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	1.760373	-0.881285	-0.887515	0.085049	0.0	0.742971	1.169781	-1.323220	1.785511	1.749610	-0.660853	2.144987	0.766074	0.523316	-0.877232	-0.426230	-0.658973	0.0	-0.932014	3.049627	-0.620189	-1.077862	0.325228	1.041095	1.804297	1.086895
1054	0	0	1	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.322365	1.704418	-0.270544	1.061787	0.0	0.762911	0.254625	-1.520046	0.379672	0.845911	-0.660853	0.842067	0.932475	0.122861	-0.330589	-0.426230	-0.658973	0.0	1.415991	2.278234	0.155707	0.338096	0.161947	0.764998	-0.679146	0.806541
1055	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	1	0	0	-0.320163	0.065732	0.716610	0.085049	0.0	0.764573	-0.660531	0.251388	0.379672	1.749610	-1.567907	2.231890	-0.335064	1.724681	-0.330589	-0.426230	1.191438	0.0	1.415991	0.606882	0.155707	-1.077862	1.141629	1.041095	1.183437	1.367249
1076	0	1	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.869874	0.613613	0.223033	1.061787	0.0	0.822730	-0.660531	1.038693	0.379672	1.749610	1.153254	2.024724	1.425492	0.122861	1.309341	2.346151	-1.584178	0.0	0.241988	1.892537	-0.620189	0.338096	1.141629	1.317193	-0.368715	2.208310
1078	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	0.774856	-1.652286	2.320735	0.085049	0.0	0.827714	1.169781	-1.667666	0.379672	1.749610	-1.567907	2.087617	1.090724	0.122861	-0.603911	-0.426230	0.266233	0.0	0.241988	1.635406	-1.396086	1.754054	2.121310	0.488900	3.666880	3.610079
1080	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.993860	-1.424209	-0.764121	0.085049	0.0	0.834361	0.254625	-0.732742	0.379672	1.749610	-0.660853	2.146686	-0.412221	2.125136	-0.877232	-0.426230	1.191438	0.0	0.241988	1.506840	-0.620189	1.754054	0.978348	2.145487	0.873006	-0.875581
1086	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	1	0	1	0	0	1	1.431867	-1.163903	1.580370	2.038524	0.0	0.854300	0.254625	1.087899	-2.432006	1.749610	1.153254	1.680296	1.424649	-0.678049	-0.603911	-0.426230	1.191438	0.0	-0.932014	2.663930	-0.620189	0.338096	4.080672	0.488900	3.356449	1.367249
1093	0	1	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	0.555852	-1.275463	-0.887515	0.085049	0.0	0.867593	1.169781	-1.274014	0.379672	0.845911	1.153254	0.769400	0.604030	-0.277594	-0.330589	-0.426230	0.266233	0.0	0.241988	1.635406	0.155707	-2.493820	2.121310	1.041095	3.356449	1.367249
1096	0	0	1	1	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.336849	0.236790	-0.393938	-0.891688	0.0	0.872578	0.254625	-1.372427	0.379672	1.749610	1.153254	2.110778	0.431165	-0.678049	1.582663	2.346151	1.191438	0.0	-0.932014	1.249709	-0.620189	0.338096	2.284590	0.764998	1.493867	0.806541
1103	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	1	1.212863	-0.769726	0.840004	1.061787	0.0	0.884210	0.254625	1.481551	0.379672	-0.057788	0.246200	-0.013584	-0.087150	2.125136	-0.330589	-0.426230	0.266233	0.0	0.241988	0.864013	-0.620189	0.338096	0.161947	0.764998	1.493867	0.806541
1111	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	0	0	0	1	1.760373	-0.484629	-0.887515	2.038524	0.0	0.909134	0.254625	0.595834	-1.026167	0.845911	1.153254	0.778961	0.042850	-1.078504	0.216054	-0.426230	-0.658973	0.0	0.241988	2.921061	0.931603	0.338096	4.243953	0.764998	-0.368715	1.367249
1116	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.979376	-0.291259	2.073946	2.038524	0.0	0.919104	0.254625	-0.289883	-1.026167	2.653309	1.153254	2.779873	1.226065	-0.678049	1.582663	2.346151	0.266233	0.0	0.241988	3.178192	0.155707	0.338096	4.733793	0.488900	-0.058285	2.488664
1126	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.431867	-1.334961	-0.023755	0.085049	0.0	0.940705	0.254625	-0.339090	0.379672	2.653309	0.246200	2.725691	0.731642	0.523316	0.216054	-0.426230	0.266233	0.0	0.241988	2.021103	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935
1129	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0.555852	0.635925	-0.023755	-0.891688	0.0	0.947352	1.169781	1.333932	-1.026167	2.653309	1.153254	2.785610	1.693363	2.125136	1.855984	2.346151	1.191438	0.0	-0.932014	1.635406	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-0.875581
1135	0	0	1	0	0	1	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0.993860	-0.593710	-1.010909	1.061787	0.0	0.958983	1.169781	-0.486709	1.785511	1.749610	-1.567907	2.350878	-1.568030	-0.678049	-0.057267	-0.426230	-0.658973	0.0	-0.932014	2.021103	1.707500	-2.493820	3.100991	-1.167687	-0.679146	2.208310
1138	0	1	0	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.431867	1.069768	1.333581	2.038524	0.0	0.965630	-0.660531	-1.224807	0.379672	1.749610	0.246200	1.007588	0.896074	-0.277594	-0.057267	-0.426230	0.266233	0.0	0.241988	2.663930	0.155707	0.338096	3.754112	1.041095	3.046019	2.488664
1140	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	1	0	0	1	0.774856	1.265617	-0.270544	0.085049	0.0	0.968953	-0.660531	-1.716872	0.379672	2.653309	1.153254	2.665772	-1.512797	-1.078504	-0.330589	-0.426230	1.191438	0.0	0.241988	1.506840	0.931603	-1.077862	2.447870	0.764998	-0.368715	1.647603
1154	0	0	1	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.103362	0.925980	2.073946	1.061787	0.0	0.997200	1.169781	1.579964	0.379672	2.653309	0.246200	2.795171	-1.277953	0.122861	-1.150554	-0.426230	0.266233	0.0	0.241988	2.021103	-0.620189	0.338096	-0.327893	-0.615492	-0.368715	-1.155935
1156	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	0	1	0	0	0.336849	0.202082	0.716610	0.085049	0.0	1.002185	-1.575686	0.694247	-1.026167	0.845911	0.246200	0.835481	1.614379	-0.678049	-0.603911	-0.426230	1.191438	0.0	1.415991	0.864013	-0.620189	0.338096	1.794749	2.973780	3.666880	2.208310
1162	0	0	1	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	1	0	0	-0.210661	-0.162346	0.099639	0.085049	0.0	1.020463	1.169781	-0.535916	-1.026167	0.845911	-1.567907	0.808071	1.014269	2.525591	0.489376	-0.426230	0.266233	0.0	-0.932014	0.478316	0.155707	0.338096	0.978348	2.145487	1.183437	-1.155935
1166	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.212863	-1.084572	-0.640727	2.038524	0.0	1.028771	0.254625	1.137106	-1.026167	1.749610	1.153254	1.848366	-1.224267	-0.277594	2.675949	2.346151	-0.658973	0.0	0.241988	1.506840	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
1176	0	0	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.243234	1.580370	1.061787	0.0	1.047049	-1.575686	0.300595	0.379672	1.749610	-0.660853	2.105678	-1.519965	0.122861	0.216054	-0.426230	-0.658973	0.0	1.415991	2.021103	-0.620189	0.338096	-0.491174	-0.615492	-0.368715	-0.595227
1177	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	0	0	1	1.431867	0.026066	0.963398	2.038524	0.0	1.048711	1.169781	-0.781948	-1.026167	0.845911	-1.567907	1.437645	1.077653	0.923771	-0.057267	-0.426230	0.266233	0.0	2.589994	0.992578	0.155707	0.338096	1.141629	1.869389	-0.368715	1.927956
1181	0	0	1	0	1	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.322365	-0.836662	-0.393938	-1.868426	0.0	1.057019	0.254625	-1.224807	-1.026167	1.749610	0.246200	1.585743	-0.373994	-0.277594	1.036019	-0.426230	-0.658973	0.0	0.241988	2.406799	0.155707	0.338096	1.304909	1.869389	-0.058285	2.208310
1184	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.869874	-0.541648	1.580370	2.038524	0.0	1.063666	-0.660531	1.235519	0.379672	1.749610	0.246200	2.320919	0.614430	0.122861	2.675949	2.346151	0.266233	0.0	0.241988	3.178192	2.483396	0.338096	0.488508	1.041095	0.562576	0.806541
1185	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	0.665354	1.211077	0.716610	-0.891688	0.0	1.065327	0.254625	-0.043851	-1.026167	1.749610	0.246200	2.358528	-1.516170	-0.678049	2.402628	2.346151	-1.584178	0.0	0.241988	0.349751	0.155707	0.338096	1.141629	1.593291	1.183437	1.927956
1194	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.103362	1.047456	-0.887515	1.061787	0.0	1.081943	-0.660531	-0.929568	1.785511	1.749610	-0.660853	2.011975	0.951869	1.324226	-0.330589	-0.426230	0.266233	0.0	2.589994	2.278234	-0.620189	0.338096	-0.654454	-0.615492	-0.368715	-0.595227
1195	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.322365	0.016150	-1.010909	0.085049	0.0	1.083605	0.254625	-1.470840	0.379672	1.749610	0.246200	1.885975	1.134291	0.523316	-0.330589	-0.426230	-1.584178	0.0	-0.932014	1.506840	-0.620189	0.338096	0.161947	0.764998	-0.679146	-1.155935
1221	1	0	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.884358	1.079684	-1.010909	-1.868426	0.0	1.141762	0.254625	0.399008	-1.026167	0.845911	0.246200	0.901986	-1.534440	0.122861	2.129306	2.346151	1.191438	0.0	0.241988	1.763972	0.155707	-1.077862	2.611150	2.973780	3.666880	-0.034520
1223	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.103362	0.720215	-0.023755	0.085049	0.0	1.148409	0.254625	0.792660	-2.432006	1.749610	0.246200	1.366890	1.384454	1.724681	-1.150554	-0.426230	0.266233	0.0	-0.932014	1.763972	0.155707	-2.493820	2.611150	0.212802	3.666880	1.647603
1225	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	0.884358	0.502054	2.320735	-0.891688	0.0	1.153393	1.169781	-0.880361	-1.026167	1.749610	-0.660853	2.167509	0.394343	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	1.249709	-0.620189	0.338096	2.284590	0.488900	1.804297	0.526188
1242	0	0	1	0	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	1	0	0	0.336849	-0.474713	-0.270544	1.061787	0.0	1.188288	-0.660531	1.087899	0.379672	2.653309	-0.660853	2.832355	-1.400364	-0.678049	-0.330589	-0.426230	-0.658973	0.0	-0.932014	1.249709	0.155707	-1.077862	2.284590	1.041095	3.046019	1.086895
1264	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.979376	-0.804433	-0.887515	0.085049	0.0	1.238136	0.254625	-0.289883	-1.026167	2.653309	-1.567907	2.663434	0.771836	2.125136	-0.877232	-0.426230	-0.658973	0.0	2.589994	2.921061	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935
1268	1	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	0	0	1	1.760373	-0.350758	-1.010909	1.061787	0.0	1.246445	-1.575686	-0.289883	-1.026167	1.749610	0.246200	1.373052	1.123610	0.523316	1.309341	2.346151	1.191438	0.0	2.589994	2.021103	-0.620189	-1.077862	-0.654454	-0.615492	-0.679146	-0.595227
1275	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.541369	0.345870	-0.764121	0.085049	0.0	1.264722	-1.575686	-0.634329	0.379672	0.845911	0.246200	1.405136	1.218616	-0.277594	-1.150554	-0.426230	1.191438	0.0	-0.932014	0.478316	-0.620189	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
1277	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	1	0	1	0	0	1	0.993860	-0.169783	-0.887515	1.061787	0.0	1.269707	0.254625	-0.978775	0.379672	2.653309	1.153254	2.725053	-0.013366	1.724681	0.489376	-0.426230	0.266233	0.0	0.241988	1.635406	0.155707	0.338096	-0.817734	-0.891589	-0.058285	-0.595227
1278	0	0	1	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	1	0	0	-0.101159	1.439154	0.099639	0.085049	0.0	1.271369	1.169781	1.186312	0.379672	0.845911	-1.567907	0.386301	1.635320	1.724681	-0.603911	-0.426230	1.191438	0.0	0.241988	0.478316	-1.396086	0.338096	0.815068	1.041095	0.873006	0.806541
1295	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	0	1	0	0	1	0.446350	-0.016079	-0.640727	-1.868426	0.0	1.312910	0.254625	0.743454	0.379672	0.845911	0.246200	0.838030	1.706855	-1.078504	-0.603911	-0.426230	1.191438	0.0	0.241988	1.506840	0.155707	1.754054	2.447870	2.697683	3.356449	0.245834
1301	1	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	2.307882	-1.121758	-0.887515	0.085049	0.0	1.327864	-0.660531	-0.683535	0.379672	1.749610	-0.660853	2.079756	1.161416	0.523316	1.855984	2.346151	1.191438	0.0	0.241988	3.306758	-2.171982	-1.077862	1.468189	1.317193	3.666880	2.769018
1303	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	1	0	0	1	1.103362	0.492137	-0.640727	0.085049	0.0	1.332849	0.254625	1.284725	-1.026167	0.845911	-0.660853	0.813808	0.696787	2.125136	-0.877232	-0.426230	0.266233	0.0	0.241988	2.149668	0.931603	0.338096	2.447870	1.869389	3.666880	1.647603
1310	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	1	2.307882	1.025144	0.716610	1.061787	0.0	1.349466	-1.575686	1.038693	0.379672	1.749610	0.246200	1.972666	1.027480	-0.277594	-0.330589	-0.426230	-0.658973	0.0	-0.932014	1.506840	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
1327	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	1	0.993860	1.280492	-0.764121	0.085049	0.0	1.392668	-1.575686	-1.027981	1.785511	1.749610	-1.567907	1.428296	-0.923931	-0.277594	-0.877232	-0.426230	1.191438	0.0	0.241988	1.763972	1.707500	0.338096	1.958030	3.525976	-0.058285	1.086895
1330	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.665354	0.050857	-0.393938	0.085049	0.0	1.397653	-1.575686	0.743454	-1.026167	2.653309	0.246200	2.738652	1.156075	1.724681	-0.603911	-0.426230	1.191438	0.0	-0.932014	1.249709	-0.620189	0.338096	1.468189	2.145487	1.183437	2.769018
1331	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.212863	1.044977	0.099639	0.085049	0.0	1.399314	1.169781	1.235519	-1.026167	2.653309	-0.660853	2.796659	-0.102609	0.523316	-0.877232	-0.426230	1.191438	0.0	-0.932014	2.278234	0.155707	0.338096	2.447870	1.593291	3.046019	1.367249
1348	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	1	0	0	0.336849	0.829295	-1.010909	1.061787	0.0	1.440855	-1.575686	1.579964	0.379672	1.749610	-1.567907	2.192794	0.657436	-0.277594	-1.150554	-0.426230	-1.584178	0.0	0.241988	1.378275	0.155707	0.338096	1.958030	0.764998	2.735589	3.329725
1351	0	1	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.212863	-1.699389	1.580370	0.085049	0.0	1.454148	1.169781	-0.388296	0.379672	1.749610	1.153254	2.267374	-1.669079	0.122861	-1.150554	-0.426230	-0.658973	0.0	0.241988	1.635406	0.155707	0.338096	2.447870	3.525976	0.562576	0.806541
1357	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	0.555852	1.471383	-0.393938	0.085049	0.0	1.472426	0.254625	0.841867	0.379672	0.845911	-1.567907	1.454431	0.074332	2.525591	-0.603911	-0.426230	-0.658973	0.0	0.241988	0.864013	0.155707	1.754054	0.978348	0.764998	0.873006	0.806541
1374	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	2.307882	-0.489587	1.456975	0.085049	0.0	1.517290	1.169781	0.300595	0.379672	1.749610	1.153254	2.416322	-0.358675	0.523316	-0.603911	-0.426230	0.266233	0.0	0.241988	2.278234	-0.620189	-1.077862	-0.981014	-1.167687	-0.679146	-1.155935
1377	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.322365	0.648321	-0.887515	-1.868426	0.0	1.522275	-0.660531	-1.175601	0.379672	2.653309	1.153254	2.689569	-0.080826	0.122861	-0.057267	-0.426230	1.191438	0.0	-0.932014	2.149668	0.155707	0.338096	-0.327893	-0.063296	0.562576	-0.314873
1401	0	0	1	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.979376	-1.520894	2.073946	1.061787	0.0	1.575447	0.254625	0.251388	1.785511	2.653309	-0.660853	2.790497	1.615925	0.523316	0.762698	-0.426230	-1.584178	0.0	0.241988	3.049627	-2.171982	0.338096	0.488508	1.317193	-0.368715	-0.034520
1403	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	1	0	0	0.227347	-1.694431	0.716610	1.061787	0.0	1.578770	-0.660531	0.546627	0.379672	1.749610	-1.567907	1.452944	1.515719	-1.078504	-0.877232	-0.426230	-1.584178	0.0	-0.932014	1.249709	0.155707	0.338096	2.121310	1.041095	2.735589	1.647603
1414	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.103362	0.935897	1.950552	0.085049	0.0	1.608679	-1.575686	0.891073	0.379672	0.845911	0.246200	0.452594	-0.172739	-0.277594	2.129306	2.346151	-0.658973	0.0	-0.932014	1.763972	0.155707	0.338096	1.631469	2.697683	3.046019	1.927956
1430	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	1	0	0	0.117845	-1.491145	0.099639	0.085049	0.0	1.645235	-0.660531	1.629171	-2.432006	0.845911	0.246200	1.424259	-1.537111	0.122861	-0.877232	-0.426230	-1.584178	0.0	0.241988	1.121144	0.155707	0.338096	1.794749	3.249878	-0.368715	1.927956
1437	1	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	1	0	0	0.227347	-1.729138	-0.023755	0.085049	0.0	1.656867	1.169781	1.038693	0.379672	2.653309	1.153254	2.746939	0.138980	-0.277594	-0.603911	-0.426230	0.266233	0.0	-0.932014	1.249709	0.155707	-1.077862	-0.164613	-1.167687	-0.368715	-0.314873
1443	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.555852	-1.245713	-0.887515	0.085049	0.0	1.671821	-1.575686	-0.486709	0.379672	2.653309	0.246200	2.629863	0.421468	0.923771	-1.150554	-0.426230	-1.584178	0.0	-0.932014	1.635406	-0.620189	-1.077862	2.447870	0.488900	0.562576	2.769018
1445	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	0	0	0	1	0.446350	-0.546607	2.320735	1.061787	0.0	1.676806	-1.575686	-0.289883	-1.026167	1.749610	-0.660853	1.501601	-1.218926	-1.078504	2.129306	2.346151	0.266233	0.0	0.241988	1.249709	0.155707	0.338096	2.121310	0.764998	-0.679146	1.647603

index_1 = np.where(y_var==1)
index_1

(array([   0,    2,    3,    4,    6,    7,   10,   12,   13,   14,   16,
          17,   19,   20,   21,   23,   24,   30,   31,   32,   33,   34,
          35,   36,   37,   38,   39,   40,   41,   42,   47,   49,   50,
          51,   52,   53,   54,   57,   66,   67,   68,   69,   70,   71,
          72,   74,   77,   78,   79,   81,   82,   84,   86,   87,   96,
          97,   99,  100,  101,  102,  104,  107,  108,  109,  113,  114,
         115,  118,  122,  125,  127,  128,  130,  131,  132,  135,  136,
         138,  140,  141,  142,  143,  145,  146,  148,  149,  156,  157,
         159,  160,  161,  162,  163,  164,  169,  170,  171,  174,  175,
         176,  177,  179,  180,  181,  182,  183,  184,  191,  192,  193,
         196,  197,  198,  200,  202,  203,  204,  206,  207,  208,  209,
         214,  215,  216,  217,  219,  221,  224,  225,  229,  230,  232,
         234,  238,  239,  240,  241,  242,  245,  246,  248,  249,  250,
         252,  253,  254,  255,  258,  259,  260,  262,  264,  265,  267,
         272,  273,  274,  275,  284,  286,  287,  288,  289,  292,  294,
         296,  298,  299,  301,  308,  309,  310,  312,  318,  320,  322,
         323,  327,  328,  330,  331,  332,  333,  335,  336,  337,  340,
         345,  346,  347,  348,  349,  350,  351,  352,  354,  356,  357,
         358,  360,  361,  362,  363,  364,  365,  368,  369,  370,  371,
         372,  373,  374,  377,  378,  380,  381,  382,  383,  385,  387,
         388,  389,  391,  393,  395,  396,  397,  399,  402,  404,  405,
         407,  409,  410,  413,  414,  415,  416,  418,  419,  421,  422,
         428,  430,  431,  433,  436,  437,  438,  439,  440,  441,  443,
         449,  450,  453,  454,  455,  456,  457,  458,  460,  461,  463,
         464,  469,  470,  471,  472,  474,  476,  478,  479,  480,  481,
         482,  483,  485,  486,  487,  488,  490,  493,  494,  495,  496,
         498,  499,  500,  501,  504,  505,  507,  511,  512,  513,  515,
         516,  517,  520,  521,  522,  525,  528,  536,  539,  540,  542,
         543,  546,  547,  548,  549,  550,  551,  553,  554,  555,  556,
         557,  559,  560,  565,  566,  570,  571,  572,  573,  574,  575,
         576,  577,  579,  580,  581,  583,  585,  586,  587,  589,  591,
         596,  597,  598,  599,  601,  602,  603,  605,  606,  613,  615,
         617,  618,  620,  622,  623,  626,  628,  629,  630,  631,  632,
         633,  634,  637,  638,  639,  640,  642,  643,  644,  645,  648,
         650,  654,  655,  656,  657,  659,  660,  661,  662,  663,  665,
         666,  667,  668,  669,  670,  671,  672,  673,  678,  679,  680,
         682,  683,  684,  688,  689,  691,  694,  697,  698,  700,  703,
         706,  709,  711,  712,  713,  715,  717,  719,  720,  722,  724,
         725,  726,  727,  731,  732,  734,  735,  737,  739,  740,  742,
         744,  747,  748,  754,  759,  761,  762,  763,  764,  765,  767,
         769,  772,  775,  776,  777,  779,  781,  782,  786,  790,  793,
         794,  795,  797,  798,  800,  801,  802,  803,  811,  815,  816,
         818,  819,  820,  822,  823,  824,  826,  827,  828,  829,  830,
         831,  832,  833,  835,  839,  840,  841,  842,  845,  848,  849,
         850,  853,  854,  856,  857,  859,  860,  862,  863,  864,  865,
         866,  868,  871,  872,  876,  877,  878,  880,  884,  885,  887,
         892,  893,  895,  897,  901,  902,  903,  906,  909,  910,  911,
         912,  915,  917,  921,  924,  925,  929,  931,  933,  934,  938,
         939,  940,  946,  952,  953,  957,  961,  965,  967,  970,  972,
         973,  974,  977,  980,  981,  982,  984,  986,  987,  988,  989,
         990,  991,  993,  996,  998, 1000, 1001, 1002, 1003, 1004, 1006,
        1011, 1012, 1013, 1014, 1015, 1016, 1017, 1019, 1020, 1021, 1022,
        1023, 1025, 1026, 1027, 1028, 1032, 1035, 1036, 1037, 1038, 1039,
        1041, 1042, 1044, 1045, 1046, 1047, 1049, 1051, 1052, 1056, 1057,
        1059, 1060, 1061, 1062, 1064, 1065, 1066, 1067, 1068, 1069, 1070,
        1071, 1072, 1074, 1075, 1077, 1079, 1082, 1083, 1088, 1091, 1092,
        1097, 1098, 1100, 1101, 1102, 1104, 1105, 1107, 1108, 1109, 1110,
        1112, 1113, 1114, 1115, 1117, 1118, 1120, 1121, 1123, 1125, 1127,
        1128, 1132, 1133, 1134, 1136, 1137, 1139, 1141, 1144, 1145, 1147,
        1151, 1152, 1153, 1158, 1161, 1164, 1165, 1167, 1168, 1169, 1170,
        1171, 1172, 1173, 1175, 1178, 1180, 1182, 1183, 1189, 1191, 1192,
        1193, 1196, 1197, 1199, 1200, 1201, 1202, 1203, 1205, 1207, 1209,
        1211, 1213, 1215, 1217, 1219, 1222, 1224, 1226, 1227, 1228, 1229,
        1230, 1233, 1234, 1236, 1237, 1238, 1241, 1243, 1245, 1246, 1247,
        1248, 1249, 1250, 1252, 1254, 1255, 1256, 1257, 1258, 1259, 1260,
        1261, 1262, 1263, 1266, 1270, 1271, 1272, 1273, 1276, 1279, 1283,
        1284, 1285, 1286, 1287, 1290, 1292, 1293, 1294, 1296, 1297, 1299,
        1302, 1305, 1306, 1307, 1308, 1309, 1311, 1312, 1313, 1315, 1317,
        1319, 1320, 1321, 1323, 1324, 1325, 1326, 1329, 1332, 1335, 1336,
        1337, 1338, 1339, 1341, 1342, 1343, 1344, 1345, 1349, 1352, 1353,
        1354, 1355, 1356, 1358, 1359, 1360, 1362, 1365, 1366, 1367, 1369,
        1371, 1375, 1376, 1378, 1379, 1380, 1381, 1382, 1383, 1387, 1388,
        1390, 1391, 1394, 1396, 1397, 1400, 1402, 1406, 1407, 1408, 1411,
        1413, 1415, 1417, 1419, 1420, 1422, 1423, 1426, 1427, 1428, 1433,
        1434, 1435, 1436, 1438, 1440, 1442, 1448, 1449, 1452, 1453, 1454,
        1455, 1456, 1457, 1458, 1459, 1460, 1461, 1464, 1465, 1467, 1469],
       dtype=int64),)

cluster_1 = x.iloc[index_1]
cluster_1

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
6	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	2.417384	1.292887	-0.764121	0.085049	0.0	-1.686328	0.254625	0.743454	1.785511	-0.961486	-1.567907	-0.814416	-0.611227	0.523316	1.309341	2.346151	-1.584178	0.0	2.589994	0.092620	0.155707	-1.077862	-0.981014	-1.167687	-0.679146	-1.155935
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1461	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	0	1	0	0	1	1.431867	-0.973012	2.320735	0.085049	0.0	1.711700	1.169781	-1.323220	-1.026167	0.845911	-1.567907	0.924509	0.319435	0.523316	-0.603911	-0.426230	-0.658973	0.0	0.241988	1.121144	0.155707	0.338096	-0.654454	-0.615492	-0.058285	-1.155935
1464	0	0	1	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	1	1	0	1	0	0	-1.196177	0.903668	-0.517332	0.085049	0.0	1.720008	1.169781	-1.766079	-1.026167	-0.961486	0.246200	-0.751522	0.992907	-1.078504	0.762698	-0.426230	1.191438	0.0	-0.932014	-0.807339	-0.620189	0.338096	-0.491174	-0.615492	-0.679146	-1.155935
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227

836 rows × 58 columns

index_2 = np.where(y_var==2)
index_2

(array([   1,    5,    8,    9,   11,   15,   22,   26,   27,   43,   44,
          46,   48,   55,   56,   58,   59,   60,   61,   64,   73,   75,
          76,   80,   83,   88,   89,   91,   92,   94,  103,  111,  116,
         117,  120,  121,  124,  129,  133,  134,  137,  139,  144,  150,
         151,  152,  153,  154,  155,  166,  167,  168,  172,  173,  185,
         188,  195,  199,  201,  205,  210,  211,  212,  220,  222,  223,
         226,  227,  228,  236,  243,  247,  251,  256,  261,  266,  269,
         276,  277,  278,  281,  282,  283,  285,  291,  293,  297,  302,
         303,  304,  305,  306,  307,  313,  315,  317,  319,  321,  324,
         325,  334,  338,  339,  341,  342,  343,  344,  353,  355,  359,
         366,  376,  384,  386,  394,  398,  403,  412,  420,  423,  426,
         432,  434,  435,  442,  444,  446,  447,  451,  452,  459,  462,
         467,  468,  475,  484,  491,  503,  506,  508,  509,  514,  518,
         519,  523,  524,  526,  527,  529,  530,  531,  532,  537,  541,
         545,  558,  562,  563,  564,  567,  569,  578,  582,  590,  593,
         594,  600,  604,  607,  608,  610,  611,  612,  614,  619,  621,
         635,  636,  641,  647,  651,  652,  658,  664,  675,  676,  681,
         685,  686,  687,  690,  692,  693,  696,  702,  704,  705,  707,
         708,  710,  718,  729,  730,  733,  751,  752,  753,  756,  757,
         768,  773,  778,  780,  783,  784,  785,  788,  791,  792,  796,
         805,  807,  808,  809,  817,  825,  834,  836,  843,  844,  846,
         847,  852,  855,  870,  873,  874,  875,  879,  881,  882,  883,
         886,  888,  889,  891,  896,  900,  905,  908,  920,  923,  927,
         928,  930,  932,  935,  941,  942,  943,  944,  948,  949,  950,
         951,  958,  959,  960,  963,  964,  968,  969,  978,  979,  983,
         985,  992,  995,  997, 1005, 1007, 1018, 1029, 1030, 1033, 1040,
        1048, 1050, 1053, 1058, 1063, 1073, 1081, 1084, 1085, 1087, 1089,
        1090, 1094, 1095, 1099, 1106, 1119, 1122, 1124, 1130, 1131, 1142,
        1143, 1146, 1148, 1149, 1150, 1155, 1157, 1159, 1160, 1163, 1174,
        1179, 1186, 1187, 1188, 1190, 1198, 1204, 1206, 1208, 1210, 1212,
        1214, 1216, 1218, 1220, 1231, 1232, 1235, 1239, 1240, 1244, 1251,
        1253, 1265, 1267, 1269, 1274, 1280, 1281, 1282, 1288, 1289, 1291,
        1298, 1300, 1304, 1314, 1316, 1318, 1322, 1328, 1333, 1334, 1340,
        1346, 1347, 1350, 1361, 1363, 1364, 1368, 1370, 1372, 1373, 1384,
        1385, 1386, 1389, 1392, 1393, 1395, 1398, 1399, 1404, 1405, 1409,
        1410, 1412, 1416, 1418, 1421, 1424, 1425, 1429, 1431, 1432, 1439,
        1441, 1444, 1446, 1447, 1450, 1451, 1462, 1463, 1466, 1468],
       dtype=int64),)

cluster_2 = x.iloc[index_2]
cluster_2

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
5	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	1	0	1	0	0	-0.539166	0.502054	-0.887515	-0.891688	0.0	-1.689652	1.169781	0.645041	0.379672	-0.961486	1.153254	-0.729850	-0.344199	-1.078504	-0.603911	-0.426230	0.266233	0.0	-0.932014	-0.421642	-0.620189	-1.077862	-0.001333	0.764998	0.252146	0.526188
8	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	1	0	0	0.117845	-1.453958	1.703764	0.085049	0.0	-1.683005	1.169781	-1.077188	-1.026167	0.845911	0.246200	0.642338	-0.776644	-1.078504	1.582663	2.346151	-0.658973	0.0	-0.932014	-0.164511	-0.620189	0.338096	0.325228	0.764998	-0.368715	1.086895
9	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	1.230910	2.197341	0.085049	0.0	-1.681343	0.254625	1.383138	0.379672	-0.057788	0.246200	-0.268983	0.318170	1.324226	-0.603911	-0.426230	-0.658973	0.0	1.415991	0.735447	0.155707	-1.077862	-0.001333	0.764998	1.493867	0.806541
11	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	-0.867672	-1.610141	0.716610	-0.891688	0.0	-1.678020	1.169781	-0.831155	-1.026167	-0.057788	0.246200	-0.490811	-0.229237	-1.078504	-0.877232	-0.426230	1.191438	0.0	-0.932014	-0.164511	0.155707	0.338096	0.325228	0.212802	-0.679146	1.086895
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1451	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	1	0	0	0.117845	-1.134154	0.099639	-0.891688	0.0	1.688438	-1.575686	1.678377	0.379672	-0.057788	1.153254	-0.246461	-1.170861	-0.678049	-1.150554	-0.426230	0.266233	0.0	0.241988	-0.164511	-1.396086	0.338096	0.488508	0.764998	-0.368715	1.367249
1462	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	1	0	0	0.227347	-0.199532	1.827158	-1.868426	0.0	1.713362	-0.660531	-0.289883	-1.026167	1.749610	1.153254	1.174597	-0.770882	-1.078504	-1.150554	-0.426230	-1.584178	0.0	0.241988	1.249709	-0.620189	-1.077862	2.121310	1.317193	2.114728	0.526188
1463	1	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	1	0	0	-0.648668	-1.183736	-0.517332	0.085049	0.0	1.715024	-0.660531	0.399008	0.379672	-0.057788	-1.567907	0.729454	-1.479348	-1.078504	1.036019	-0.426230	-0.658973	0.0	-0.932014	-0.164511	-0.620189	0.338096	0.325228	-0.063296	-0.368715	0.806541
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895

406 rows × 58 columns

Elbow method

WCSS_list = []
for k in range(1,10):
    kmeans_model = KMeans(n_clusters=k)  
    kmeans_model.fit(X_principal)
    WCSS = kmeans_model.inertia_
    WCSS_list.append(WCSS)
    print('K >>',k,'WCSS>>',WCSS)

K >> 1 WCSS>> 9899.510474678413
K >> 2 WCSS>> 5212.972971501031
K >> 3 WCSS>> 3686.2254848544867
K >> 4 WCSS>> 2494.5912560339857
K >> 5 WCSS>> 1860.692374538649
K >> 6 WCSS>> 1600.207985793003
K >> 7 WCSS>> 1410.9852036822958
K >> 8 WCSS>> 1250.0847066717606
K >> 9 WCSS>> 1132.191839323547

WCSS

1132.191839323547

WCSS_list

[9899.510474678413,
 5212.972971501031,
 3686.2254848544867,
 2494.5912560339857,
 1860.692374538649,
 1600.207985793003,
 1410.9852036822958,
 1250.0847066717606,
 1132.191839323547]

k = range(1,10)
plt.plot(k,WCSS_list)
plt.xlabel('Number of clusters(K)')
plt.ylabel('WCSS')
plt.title('Elbow method graph')

Text(0.5, 1.0, 'Elbow method graph')

The Elbow or or the optimal value is at 5.

kmeans_model = KMeans(n_clusters=5) 
kmeans_model.fit(X_principal)

KMeans(n_clusters=5)

kmeans_model.inertia_

1860.680999645056

y_means = kmeans_model.fit_predict(X_principal)
y_means

array([0, 1, 0, ..., 4, 1, 4])

Grouping of employees

index_0 = np.where(y_means==0)
index_0

(array([   0,    2,    4,    6,   19,   31,   33,   35,   36,   38,   52,
          53,   54,   66,   78,   79,   87,   99,  104,  114,  115,  122,
         130,  132,  135,  141,  142,  156,  157,  159,  174,  175,  183,
         184,  192,  193,  196,  197,  198,  203,  204,  208,  215,  216,
         219,  225,  230,  232,  234,  240,  242,  245,  248,  249,  250,
         253,  254,  258,  265,  273,  275,  286,  287,  291,  298,  299,
         308,  310,  322,  327,  330,  332,  333,  340,  346,  347,  352,
         354,  356,  358,  360,  364,  365,  368,  371,  372,  378,  387,
         388,  389,  391,  393,  395,  396,  409,  413,  428,  431,  433,
         438,  439,  440,  441,  450,  454,  456,  458,  464,  469,  471,
         472,  482,  483,  486,  488,  493,  499,  500,  504,  511,  520,
         525,  528,  539,  542,  543,  547,  548,  549,  551,  554,  556,
         557,  559,  560,  566,  570,  572,  573,  583,  587,  591,  596,
         597,  599,  601,  603,  605,  617,  622,  623,  626,  628,  632,
         639,  643,  648,  650,  654,  660,  661,  665,  667,  669,  672,
         678,  679,  684,  691,  700,  703,  708,  713,  715,  717,  719,
         735,  737,  742,  744,  747,  748,  759,  761,  767,  772,  775,
         782,  790,  795,  801,  811,  816,  822,  824,  829,  830,  832,
         839,  840,  845,  849,  854,  857,  862,  864,  865,  866,  877,
         878,  884,  895,  897,  901,  903,  925,  931,  938,  943,  946,
         953,  957,  961,  970,  973,  986,  988,  989,  990, 1000, 1002,
        1006, 1011, 1013, 1014, 1015, 1019, 1020, 1023, 1026, 1028, 1035,
        1036, 1037, 1038, 1042, 1044, 1046, 1049, 1051, 1057, 1062, 1065,
        1066, 1067, 1071, 1074, 1075, 1077, 1079, 1083, 1088, 1092, 1098,
        1101, 1105, 1107, 1109, 1112, 1113, 1114, 1117, 1120, 1121, 1123,
        1128, 1133, 1144, 1145, 1147, 1158, 1161, 1164, 1165, 1167, 1171,
        1172, 1173, 1183, 1191, 1192, 1196, 1199, 1200, 1202, 1203, 1207,
        1211, 1219, 1226, 1228, 1229, 1234, 1236, 1237, 1241, 1243, 1246,
        1250, 1254, 1255, 1257, 1259, 1261, 1262, 1263, 1270, 1279, 1284,
        1285, 1286, 1287, 1290, 1292, 1293, 1294, 1296, 1299, 1306, 1308,
        1315, 1319, 1321, 1324, 1325, 1326, 1335, 1336, 1342, 1344, 1352,
        1354, 1355, 1356, 1358, 1359, 1360, 1362, 1366, 1369, 1371, 1378,
        1380, 1388, 1391, 1394, 1397, 1406, 1419, 1422, 1427, 1434, 1435,
        1440, 1442, 1448, 1452, 1453, 1454, 1455, 1456, 1457, 1459, 1465],
       dtype=int64),)

Group_0 = x.iloc[index_0]
Group_0

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	0.726020	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	-1.674841	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	0.325900	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
6	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	2.417384	1.292887	-0.764121	0.085049	0.0	-1.686328	0.254625	0.743454	1.785511	-0.961486	-1.567907	-0.814416	-0.611227	0.523316	1.309341	2.346151	-1.584178	0.0	2.589994	0.092620	0.155707	-1.077862	-0.981014	-1.167687	-0.679146	-1.155935
19	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	0	1	1	0	0	0.117845	-1.069697	-0.887515	0.085049	0.0	-1.663066	1.169781	-1.027981	0.379672	-0.961486	1.153254	-0.543718	-1.406408	0.923771	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.678774	0.155707	0.338096	-0.654454	-0.615492	-0.368715	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1455	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	1	0	1	0	0	0.336849	1.287929	-0.887515	1.061787	0.0	1.700069	0.254625	-0.683535	-1.026167	-0.961486	0.246200	-0.784882	-1.628603	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	-0.421642	-0.620189	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
1456	0	1	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	1	0	0	-0.210661	0.982999	1.086793	1.061787	0.0	1.701731	0.254625	0.694247	0.379672	-0.057788	0.246200	-0.172943	1.444887	-0.678049	-0.330589	-0.426230	1.191438	0.0	1.415991	-0.164511	-0.620189	1.754054	0.488508	-0.615492	-0.679146	-0.595227
1457	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	1	0	0	0.336849	0.970604	-0.887515	1.061787	0.0	1.705054	0.254625	1.579964	0.379672	-0.961486	0.246200	-0.956564	-0.247929	-0.277594	-0.330589	-0.426230	-0.658973	0.0	2.589994	1.121144	-0.620189	0.338096	-0.327893	-0.339394	-0.679146	-0.595227
1459	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	0	1	1	0	0	-0.867672	1.426759	0.469821	-0.891688	0.0	1.708377	1.169781	-0.978775	-1.026167	-0.057788	-0.660853	-0.526508	1.316292	0.523316	-0.603911	-0.426230	-1.584178	0.0	0.241988	-0.164511	-0.620189	0.338096	-0.491174	-0.339394	-0.679146	-0.314873
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	-0.284329	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873

374 rows × 58 columns

index_1 = np.where(y_means==1)
index_1

(array([   1,    5,    8,    9,   11,   15,   22,   26,   27,   43,   44,
          46,   48,   56,   58,   59,   60,   61,   73,   75,   76,   80,
          83,   88,   89,   91,   92,   94,  103,  111,  116,  117,  120,
         121,  124,  129,  133,  134,  137,  139,  144,  150,  151,  152,
         153,  154,  155,  158,  166,  167,  168,  172,  173,  185,  188,
         195,  199,  201,  205,  210,  211,  212,  220,  222,  223,  226,
         227,  228,  236,  243,  247,  256,  261,  266,  269,  271,  276,
         277,  281,  282,  283,  285,  293,  297,  303,  304,  305,  306,
         307,  313,  315,  317,  319,  321,  324,  325,  334,  338,  339,
         341,  342,  343,  344,  353,  355,  359,  361,  366,  374,  384,
         386,  394,  398,  403,  412,  420,  423,  426,  432,  434,  435,
         442,  444,  446,  447,  451,  452,  459,  462,  467,  468,  484,
         491,  502,  503,  506,  508,  509,  514,  518,  519,  523,  524,
         526,  527,  529,  530,  531,  532,  537,  540,  545,  558,  562,
         564,  567,  569,  578,  582,  590,  593,  594,  600,  604,  607,
         608,  610,  611,  612,  619,  621,  636,  641,  647,  651,  652,
         658,  664,  675,  676,  685,  686,  687,  690,  692,  693,  695,
         696,  702,  704,  705,  707,  710,  718,  729,  730,  751,  752,
         753,  756,  757,  768,  773,  778,  780,  783,  784,  785,  788,
         791,  792,  796,  805,  807,  809,  817,  825,  836,  843,  844,
         846,  847,  852,  855,  870,  872,  873,  874,  879,  881,  882,
         883,  886,  888,  889,  891,  896,  900,  905,  908,  920,  923,
         927,  928,  930,  932,  935,  941,  942,  944,  948,  949,  950,
         951,  958,  959,  960,  963,  964,  968,  969,  978,  979,  983,
         985,  992,  995,  997, 1005, 1007, 1018, 1029, 1030, 1033, 1040,
        1048, 1050, 1053, 1058, 1063, 1073, 1081, 1084, 1085, 1087, 1089,
        1090, 1094, 1095, 1099, 1106, 1119, 1122, 1124, 1130, 1131, 1142,
        1143, 1146, 1148, 1150, 1155, 1157, 1159, 1160, 1162, 1163, 1174,
        1179, 1186, 1187, 1188, 1190, 1204, 1206, 1208, 1210, 1212, 1214,
        1216, 1218, 1220, 1232, 1240, 1244, 1251, 1253, 1260, 1265, 1267,
        1269, 1274, 1280, 1281, 1282, 1288, 1289, 1291, 1298, 1300, 1304,
        1314, 1316, 1318, 1322, 1328, 1333, 1334, 1340, 1341, 1346, 1347,
        1350, 1361, 1363, 1364, 1368, 1372, 1373, 1384, 1385, 1386, 1389,
        1392, 1393, 1395, 1398, 1399, 1404, 1405, 1409, 1410, 1412, 1416,
        1418, 1421, 1424, 1425, 1429, 1431, 1432, 1439, 1441, 1444, 1446,
        1447, 1450, 1451, 1463, 1466, 1468], dtype=int64),)

Group_1 = x.iloc[index_1]
Group_1

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	1.488876	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
5	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	1	0	1	0	0	-0.539166	0.502054	-0.887515	-0.891688	0.0	-1.689652	1.169781	0.645041	0.379672	-0.961486	1.153254	-0.729850	-0.344199	-1.078504	-0.603911	-0.426230	0.266233	0.0	-0.932014	-0.421642	-0.620189	-1.077862	-0.001333	0.764998	0.252146	0.526188
8	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	1	0	0	0.117845	-1.453958	1.703764	0.085049	0.0	-1.683005	1.169781	-1.077188	-1.026167	0.845911	0.246200	0.642338	-0.776644	-1.078504	1.582663	2.346151	-0.658973	0.0	-0.932014	-0.164511	-0.620189	0.338096	0.325228	0.764998	-0.368715	1.086895
9	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.101159	1.230910	2.197341	0.085049	0.0	-1.681343	0.254625	1.383138	0.379672	-0.057788	0.246200	-0.268983	0.318170	1.324226	-0.603911	-0.426230	-0.658973	0.0	1.415991	0.735447	0.155707	-1.077862	-0.001333	0.764998	1.493867	0.806541
11	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	-0.867672	-1.610141	0.716610	-0.891688	0.0	-1.678020	1.169781	-0.831155	-1.026167	-0.057788	0.246200	-0.490811	-0.229237	-1.078504	-0.877232	-0.426230	1.191438	0.0	-0.932014	-0.164511	0.155707	0.338096	0.325228	0.212802	-0.679146	1.086895
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1450	0	0	1	1	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	1	1	0	0	-0.210661	0.851607	2.073946	1.061787	0.0	1.686776	0.254625	-1.716872	0.379672	0.845911	1.153254	0.495940	0.327305	-0.678049	0.216054	-0.426230	0.266233	0.0	-0.932014	-0.293077	-0.620189	0.338096	0.325228	-1.167687	-0.368715	0.806541
1451	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	1	0	0	0.117845	-1.134154	0.099639	-0.891688	0.0	1.688438	-1.575686	1.678377	0.379672	-0.057788	1.153254	-0.246461	-1.170861	-0.678049	-1.150554	-0.426230	0.266233	0.0	0.241988	-0.164511	-1.396086	0.338096	0.488508	0.764998	-0.368715	1.367249
1463	1	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	1	0	0	-0.648668	-1.183736	-0.517332	0.085049	0.0	1.715024	-0.660531	0.399008	0.379672	-0.057788	-1.567907	0.729454	-1.479348	-1.078504	1.036019	-0.426230	-0.658973	0.0	-0.932014	-0.164511	-0.620189	0.338096	0.325228	-0.063296	-0.368715	0.806541
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	1.004010	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.150393	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895

391 rows × 58 columns

index_2 = np.where(y_means==2)
index_2

(array([  18,   25,   28,   45,   55,   62,   63,   64,   90,   93,   98,
         110,  119,  123,  126,  178,  186,  187,  190,  194,  218,  231,
         235,  237,  244,  251,  257,  263,  268,  270,  295,  300,  311,
         314,  326,  390,  400,  411,  417,  425,  427,  445,  448,  466,
         473,  477,  533,  535,  538,  544,  561,  584,  592,  595,  616,
         635,  649,  653,  677,  681,  699,  701,  716,  736,  738,  746,
         749,  750,  760,  799,  810,  813,  814,  837,  838,  858,  861,
         875,  894,  907,  913,  914,  918,  922,  926,  937,  954,  955,
         962,  975,  976,  999, 1008, 1009, 1024, 1043, 1055, 1076, 1078,
        1086, 1093, 1096, 1111, 1116, 1135, 1138, 1140, 1156, 1177, 1181,
        1184, 1185, 1221, 1223, 1225, 1242, 1295, 1301, 1303, 1327, 1330,
        1331, 1348, 1351, 1403, 1414, 1430, 1443, 1445, 1462], dtype=int64),)

Group_2 = x.iloc[index_2]
Group_2

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
18	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.760373	1.032581	-0.887515	1.061787	0.0	-1.664727	-1.575686	0.595834	-1.026167	1.749610	1.153254	1.896174	1.083275	-0.277594	0.216054	-0.426230	0.266233	0.0	-0.932014	2.535365	0.155707	0.338096	2.937711	1.041095	0.252146	0.806541
25	0	0	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.760373	1.188765	-0.517332	0.085049	0.0	-1.649772	0.254625	-0.388296	0.379672	2.653309	0.246200	2.675333	-0.502870	0.523316	-1.150554	-0.426230	1.191438	0.0	0.241988	1.892537	0.155707	-1.077862	1.141629	2.421585	0.562576	1.086895
28	0	0	1	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.774856	-0.806912	-0.270544	1.061787	0.0	-1.643126	-1.575686	-1.175601	-1.026167	0.845911	1.153254	0.795747	-1.717284	0.122861	-0.330589	-0.426230	1.191438	0.0	0.241988	1.635406	0.931603	0.338096	2.447870	0.488900	0.873006	3.610079
45	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	0.446350	1.382135	0.346427	0.085049	0.0	-1.606570	-0.660531	-0.831155	0.379672	2.653309	0.246200	2.771161	0.276429	-0.678049	-0.877232	-0.426230	1.191438	0.0	-0.932014	1.506840	-2.171982	0.338096	2.447870	2.973780	3.977310	1.086895
55	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	1	1	0	0	-0.429664	-0.712706	-1.010909	-0.891688	0.0	-1.581646	-1.575686	1.579964	0.379672	0.845911	1.153254	1.477804	0.117056	-0.678049	-0.877232	-0.426230	0.266233	0.0	-0.932014	0.478316	-1.396086	0.338096	1.304909	2.697683	1.804297	2.208310
62	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	1	0	0	1	1.431867	0.462388	-0.270544	-0.891688	0.0	-1.570014	-0.660531	-1.126394	-1.026167	2.653309	0.246200	2.600116	0.335597	0.923771	-0.877232	-0.426230	1.191438	0.0	0.241988	2.278234	-0.620189	-1.077862	3.264271	-0.339394	3.356449	1.086895
63	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	1	2.417384	1.568068	1.950552	0.085049	0.0	-1.568353	-1.575686	1.629171	0.379672	0.845911	-1.567907	0.240965	-1.680744	1.724681	-1.150554	-0.426230	1.191438	0.0	-0.932014	2.149668	0.155707	-1.077862	2.284590	3.249878	1.493867	1.367249
64	0	0	1	0	1	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	1	0	0	1	1	0	1	0	0	-0.101159	1.042498	-0.147150	0.085049	0.0	-1.565029	0.254625	-0.339090	0.379672	0.845911	0.246200	0.763450	-0.858860	-0.678049	-0.603911	-0.426230	-0.658973	0.0	2.589994	0.735447	-0.620189	0.338096	1.631469	2.697683	3.046019	1.086895
90	0	1	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.336849	-0.675520	-1.010909	1.061787	0.0	-1.505211	0.254625	0.595834	-1.026167	1.749610	-0.660853	1.487365	-0.027842	-0.678049	1.855984	2.346151	1.191438	0.0	0.241988	1.378275	0.155707	-1.077862	2.447870	-0.339394	2.735589	1.927956
93	0	1	0	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	0.993860	-0.407777	-1.010909	0.085049	0.0	-1.496903	0.254625	-1.274014	-1.026167	0.845911	-1.567907	0.886051	-1.569997	-0.277594	-0.603911	-0.426230	0.266233	0.0	0.241988	1.249709	1.707500	-1.077862	0.488508	1.317193	2.114728	0.245834
98	0	0	1	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	1	2.307882	-0.298696	0.099639	1.061787	0.0	-1.485271	1.169781	-1.421633	0.379672	1.749610	0.246200	1.565770	1.418887	-1.078504	-0.603911	-0.426230	0.266233	0.0	-0.932014	3.435323	-1.396086	-1.077862	4.897073	1.593291	-0.368715	1.086895
110	0	1	0	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.541369	1.620129	-1.010909	1.061787	0.0	-1.462009	-1.575686	-1.766079	-1.026167	0.845911	-1.567907	0.208456	1.613817	0.122861	1.309341	2.346151	0.266233	0.0	-0.932014	1.506840	-1.396086	-1.077862	0.978348	2.145487	3.046019	1.086895
119	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	0.665354	-1.012678	2.073946	-0.891688	0.0	-1.440407	0.254625	1.284725	0.379672	1.749610	1.153254	2.221691	0.728128	-0.678049	-0.877232	-0.426230	1.191438	0.0	1.415991	1.763972	0.155707	1.754054	2.937711	2.145487	0.562576	2.208310
123	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	1.541369	-0.293738	-0.393938	0.085049	0.0	-1.433761	-1.575686	-0.732742	0.379672	2.653309	0.246200	2.769461	-1.103401	1.724681	-0.603911	-0.426230	0.266233	0.0	-0.932014	1.506840	1.707500	0.338096	2.121310	3.802074	3.977310	3.049371
126	0	0	1	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	1	2.307882	-1.625016	1.703764	1.061787	0.0	-1.428776	1.169781	1.383138	0.379672	0.845911	1.153254	0.809346	-1.524603	-0.678049	-0.877232	-0.426230	1.191438	0.0	0.241988	3.692454	0.155707	-1.077862	5.386914	1.593291	3.977310	0.526188
178	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	1	0.993860	-0.685436	-1.010909	-0.891688	0.0	-1.297507	-0.660531	1.284725	0.379672	0.845911	-1.567907	0.839305	-1.711241	-0.678049	2.675949	2.346151	0.266233	0.0	2.589994	1.635406	-0.620189	0.338096	2.774431	2.421585	3.977310	0.806541
186	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	1	0	0	0.336849	0.462388	-0.640727	-1.868426	0.0	-1.282553	1.169781	-0.978775	0.379672	2.653309	0.246200	2.662372	-1.098201	-0.678049	-0.330589	-0.426230	-0.658973	0.0	0.241988	1.249709	-0.620189	0.338096	2.121310	1.041095	2.114728	1.367249
187	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	1.979376	-0.273905	0.593216	1.061787	0.0	-1.280891	0.254625	-0.240677	1.785511	2.653309	-0.660853	2.596291	-0.136901	2.125136	-1.150554	-0.426230	1.191438	0.0	-0.932014	3.178192	0.155707	0.338096	2.774431	2.973780	-0.058285	3.049371
190	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.650871	-0.256552	-1.010909	1.061787	0.0	-1.272583	0.254625	-0.043851	-1.026167	2.653309	0.246200	2.867626	-1.213586	-1.078504	-0.330589	-0.426230	-1.584178	0.0	0.241988	2.921061	1.707500	0.338096	4.243953	3.802074	2.735589	1.367249
194	1	0	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.884358	0.973083	-0.887515	-0.891688	0.0	-1.264275	-1.575686	-0.043851	-1.026167	1.749610	1.153254	2.186207	0.864172	2.525591	2.129306	2.346151	1.191438	0.0	0.241988	1.378275	-1.396086	0.338096	2.121310	1.041095	2.735589	1.086895
218	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	1	0.884358	0.618571	-0.393938	0.085049	0.0	-1.201133	1.169781	-0.437503	-1.026167	0.845911	1.153254	0.501889	0.355132	1.324226	-0.877232	-0.426230	1.191438	0.0	-0.932014	1.506840	-0.620189	0.338096	1.958030	0.764998	3.046019	1.086895
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411	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	2.526886	-0.943263	-0.270544	0.085049	0.0	-0.790711	-1.575686	-1.224807	0.379672	2.653309	-1.567907	2.775623	-1.469932	0.923771	-1.150554	-0.426230	1.191438	0.0	-0.932014	2.792496	1.707500	-2.493820	3.590832	1.041095	2.735589	1.647603
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425	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.431867	0.735089	2.444129	1.061787	0.0	-0.757478	-0.660531	1.087899	-1.026167	1.749610	0.246200	2.240177	-0.702579	-1.078504	-0.057267	-0.426230	-0.658973	0.0	0.241988	2.149668	-0.620189	0.338096	3.264271	1.593291	3.977310	0.806541
427	0	1	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	2.526886	1.726730	2.320735	0.085049	0.0	-0.750832	0.254625	0.694247	-1.026167	0.845911	-1.567907	0.799572	-1.611738	0.523316	1.036019	-0.426230	1.191438	0.0	-0.932014	1.378275	1.707500	1.754054	1.794749	2.421585	3.356449	1.927956
445	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.979376	0.779713	1.086793	2.038524	0.0	-0.710953	-1.575686	0.841867	0.379672	1.749610	-0.660853	2.195344	-0.624016	0.122861	2.129306	2.346151	1.191438	0.0	-0.932014	3.306758	-0.620189	0.338096	0.488508	1.317193	1.493867	0.806541
448	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	1	0	0	0.336849	-1.240755	-0.393938	0.085049	0.0	-0.704306	-0.660531	0.448214	0.379672	1.749610	0.246200	1.430846	0.850399	1.724681	-0.057267	-0.426230	0.266233	0.0	-0.932014	1.378275	0.155707	0.338096	2.121310	0.488900	0.873006	2.488664
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535	0	0	1	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	0.446350	-0.930867	0.099639	1.061787	0.0	-0.488295	-0.660531	0.349801	-1.026167	2.653309	1.153254	2.685320	-0.766244	0.122861	-0.057267	-0.426230	-0.658973	0.0	2.589994	1.506840	-0.620189	-1.077862	2.284590	0.488900	3.046019	0.526188
538	0	0	1	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.446350	-1.211006	-1.010909	0.085049	0.0	-0.483310	1.169781	-0.339090	-1.026167	2.653309	0.246200	2.695519	0.737685	-0.678049	-0.877232	-0.426230	-0.658973	0.0	0.241988	1.378275	0.155707	0.338096	2.447870	0.764998	-0.058285	1.647603
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561	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.650871	-0.449922	-0.764121	1.061787	0.0	-0.413521	0.254625	-1.716872	-1.026167	1.749610	-1.567907	2.199806	-0.594362	-0.678049	-1.150554	-0.426230	-1.584178	0.0	-0.932014	2.921061	0.155707	1.754054	4.407233	0.488900	-0.368715	3.329725
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592	0	0	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.103362	-1.488665	-0.887515	-0.891688	0.0	-0.340410	0.254625	-1.618459	0.379672	1.749610	1.153254	2.177708	-0.187075	-0.678049	-1.150554	-0.426230	0.266233	0.0	0.241988	1.892537	0.155707	-1.077862	3.100991	2.697683	0.252146	-1.155935
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746	1	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	0	0	1	0.446350	-1.377106	-0.270544	-1.868426	0.0	0.016840	-0.660531	-0.535916	-2.432006	2.653309	0.246200	2.862102	0.839155	-0.678049	1.855984	2.346151	-0.658973	0.0	1.415991	1.249709	0.155707	0.338096	2.284590	3.249878	0.873006	1.647603
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750	0	0	1	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	1	0	0	1	0.774856	1.600296	2.320735	0.085049	0.0	0.023487	1.169781	-0.634329	1.785511	1.749610	1.153254	1.448482	-0.362048	0.122861	0.762698	-0.426230	0.266233	0.0	0.241988	1.506840	-0.620189	0.338096	0.815068	1.869389	2.735589	1.927956
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810	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.993860	-0.982929	-0.764121	-1.868426	0.0	0.164725	-1.575686	-0.683535	0.379672	1.749610	0.246200	2.329206	0.180299	0.122861	-0.877232	-0.426230	1.191438	0.0	0.241988	1.506840	0.155707	0.338096	0.815068	1.317193	0.562576	1.367249
813	0	1	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1	1	0	1	0	0	0.227347	-1.486186	-0.887515	0.085049	0.0	0.169710	-1.575686	0.891073	0.379672	1.749610	1.153254	1.203919	-0.107669	1.724681	-1.150554	-0.426230	1.191438	0.0	2.589994	1.249709	0.931603	0.338096	1.794749	0.764998	2.735589	0.245834
814	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	1	0	0	0.336849	1.253222	0.593216	0.085049	0.0	0.171371	0.254625	-1.077188	-1.026167	2.653309	0.246200	2.788372	0.454073	-0.678049	-0.330589	-0.426230	-1.584178	0.0	-0.932014	1.249709	-0.620189	1.754054	2.121310	0.764998	0.562576	1.367249
837	0	1	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	1	0	0	0.336849	-0.519337	-0.023755	1.061787	0.0	0.234513	-0.660531	1.087899	0.379672	0.845911	0.246200	1.486515	-0.074642	2.525591	0.489376	-0.426230	0.266233	0.0	-0.932014	1.121144	0.155707	-1.077862	1.794749	0.764998	-0.058285	2.488664
838	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	0.555852	-0.796996	0.346427	0.085049	0.0	0.236175	0.254625	-1.077188	0.379672	1.749610	-1.567907	1.541547	-1.667673	-1.078504	-0.877232	-0.426230	-0.658973	0.0	-0.932014	1.378275	-0.620189	-1.077862	2.284590	1.317193	3.356449	2.769018
858	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.760373	1.042498	-0.270544	-0.891688	0.0	0.292670	1.169781	-0.781948	0.379672	2.653309	0.246200	2.571644	0.608106	0.122861	0.762698	-0.426230	-0.658973	0.0	0.241988	1.892537	2.483396	0.338096	-0.001333	0.764998	0.562576	0.806541
861	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.993860	1.486257	-0.887515	0.085049	0.0	0.297655	0.254625	0.152975	0.379672	1.749610	-1.567907	2.240602	1.375038	2.125136	2.129306	2.346151	-1.584178	0.0	-0.932014	2.149668	-0.620189	0.338096	3.100991	2.973780	3.977310	1.367249
875	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	1	0	0	0	1	0.774856	-1.493624	2.444129	1.061787	0.0	0.332549	1.169781	-1.667666	0.379672	-0.057788	1.153254	-0.416869	-0.923228	-0.678049	2.675949	2.346151	-0.658973	0.0	-0.932014	1.121144	0.155707	0.338096	2.121310	1.869389	3.356449	3.610079
894	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.869874	-0.291259	-0.764121	0.085049	0.0	0.374090	1.169781	0.940280	0.379672	1.749610	1.153254	2.395924	1.287481	0.122861	-0.330589	-0.426230	-1.584178	0.0	-0.932014	3.178192	-0.620189	0.338096	0.488508	1.317193	-0.679146	1.367249
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937	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0.227347	-0.968054	0.469821	1.061787	0.0	0.468803	0.254625	1.383138	-1.026167	1.749610	-0.660853	2.256538	0.424560	1.324226	-0.603911	-0.426230	1.191438	0.0	1.415991	1.249709	0.931603	0.338096	1.958030	1.317193	3.977310	-0.595227
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955	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.979376	-1.290337	-0.887515	-0.891688	0.0	0.516990	1.169781	-0.388296	-2.432006	2.653309	0.246200	2.695094	-1.028915	0.523316	-0.330589	-0.426230	1.191438	0.0	0.241988	1.506840	1.707500	0.338096	1.958030	1.317193	2.114728	1.927956
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975	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	1.979376	-1.327524	0.469821	1.061787	0.0	0.576809	-1.575686	0.940280	1.785511	1.749610	0.246200	1.528161	-0.707779	1.324226	0.489376	-0.426230	0.266233	0.0	-0.932014	1.635406	-0.620189	-1.077862	1.958030	0.764998	0.252146	1.086895
976	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	0	0	1	2.088878	1.404447	1.703764	0.085049	0.0	0.578470	1.169781	0.103769	0.379672	1.749610	-0.660853	1.465905	0.551187	0.523316	-0.877232	-0.426230	-1.584178	0.0	0.241988	2.792496	-2.171982	0.338096	1.958030	3.249878	3.977310	1.367249
999	0	0	1	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.555852	0.854086	0.099639	0.085049	0.0	0.636627	0.254625	-1.716872	0.379672	1.749610	-1.567907	2.187695	0.323651	-1.078504	-0.330589	-0.426230	0.266233	0.0	0.241988	1.249709	1.707500	0.338096	2.121310	0.764998	-0.679146	1.367249
1008	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	1.869874	0.417764	-1.010909	0.085049	0.0	0.659890	1.169781	-0.585122	0.379672	1.749610	1.153254	2.300096	-1.217240	1.324226	1.036019	-0.426230	1.191438	0.0	-0.932014	2.278234	0.155707	-1.077862	2.121310	0.764998	3.046019	0.806541
1009	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	1	2.307882	0.626009	-1.010909	0.085049	0.0	0.661552	1.169781	0.497421	0.379672	2.653309	-1.567907	2.804308	1.144410	0.122861	1.582663	2.346151	0.266233	0.0	0.241988	2.663930	0.155707	0.338096	0.325228	1.041095	-0.368715	0.245834
1024	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.103362	-1.099446	-0.887515	1.061787	0.0	0.694784	-1.575686	0.792660	0.379672	1.749610	0.246200	2.266312	1.741288	0.122861	1.036019	-0.426230	-0.658973	0.0	1.415991	1.892537	-0.620189	1.754054	2.121310	3.525976	0.873006	0.526188
1043	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	1.760373	-0.881285	-0.887515	0.085049	0.0	0.742971	1.169781	-1.323220	1.785511	1.749610	-0.660853	2.144987	0.766074	0.523316	-0.877232	-0.426230	-0.658973	0.0	-0.932014	3.049627	-0.620189	-1.077862	0.325228	1.041095	1.804297	1.086895
1055	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	1	0	0	-0.320163	0.065732	0.716610	0.085049	0.0	0.764573	-0.660531	0.251388	0.379672	1.749610	-1.567907	2.231890	-0.335064	1.724681	-0.330589	-0.426230	1.191438	0.0	1.415991	0.606882	0.155707	-1.077862	1.141629	1.041095	1.183437	1.367249
1076	0	1	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.869874	0.613613	0.223033	1.061787	0.0	0.822730	-0.660531	1.038693	0.379672	1.749610	1.153254	2.024724	1.425492	0.122861	1.309341	2.346151	-1.584178	0.0	0.241988	1.892537	-0.620189	0.338096	1.141629	1.317193	-0.368715	2.208310
1078	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	0.774856	-1.652286	2.320735	0.085049	0.0	0.827714	1.169781	-1.667666	0.379672	1.749610	-1.567907	2.087617	1.090724	0.122861	-0.603911	-0.426230	0.266233	0.0	0.241988	1.635406	-1.396086	1.754054	2.121310	0.488900	3.666880	3.610079
1086	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	1	0	1	0	0	1	1.431867	-1.163903	1.580370	2.038524	0.0	0.854300	0.254625	1.087899	-2.432006	1.749610	1.153254	1.680296	1.424649	-0.678049	-0.603911	-0.426230	1.191438	0.0	-0.932014	2.663930	-0.620189	0.338096	4.080672	0.488900	3.356449	1.367249
1093	0	1	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	0.555852	-1.275463	-0.887515	0.085049	0.0	0.867593	1.169781	-1.274014	0.379672	0.845911	1.153254	0.769400	0.604030	-0.277594	-0.330589	-0.426230	0.266233	0.0	0.241988	1.635406	0.155707	-2.493820	2.121310	1.041095	3.356449	1.367249
1096	0	0	1	1	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	0	1	1	0	0	0.336849	0.236790	-0.393938	-0.891688	0.0	0.872578	0.254625	-1.372427	0.379672	1.749610	1.153254	2.110778	0.431165	-0.678049	1.582663	2.346151	1.191438	0.0	-0.932014	1.249709	-0.620189	0.338096	2.284590	0.764998	1.493867	0.806541
1111	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	0	0	0	1	1.760373	-0.484629	-0.887515	2.038524	0.0	0.909134	0.254625	0.595834	-1.026167	0.845911	1.153254	0.778961	0.042850	-1.078504	0.216054	-0.426230	-0.658973	0.0	0.241988	2.921061	0.931603	0.338096	4.243953	0.764998	-0.368715	1.367249
1116	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.979376	-0.291259	2.073946	2.038524	0.0	0.919104	0.254625	-0.289883	-1.026167	2.653309	1.153254	2.779873	1.226065	-0.678049	1.582663	2.346151	0.266233	0.0	0.241988	3.178192	0.155707	0.338096	4.733793	0.488900	-0.058285	2.488664
1135	0	0	1	0	0	1	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0.993860	-0.593710	-1.010909	1.061787	0.0	0.958983	1.169781	-0.486709	1.785511	1.749610	-1.567907	2.350878	-1.568030	-0.678049	-0.057267	-0.426230	-0.658973	0.0	-0.932014	2.021103	1.707500	-2.493820	3.100991	-1.167687	-0.679146	2.208310
1138	0	1	0	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.431867	1.069768	1.333581	2.038524	0.0	0.965630	-0.660531	-1.224807	0.379672	1.749610	0.246200	1.007588	0.896074	-0.277594	-0.057267	-0.426230	0.266233	0.0	0.241988	2.663930	0.155707	0.338096	3.754112	1.041095	3.046019	2.488664
1140	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1	0	1	0	0	1	0.774856	1.265617	-0.270544	0.085049	0.0	0.968953	-0.660531	-1.716872	0.379672	2.653309	1.153254	2.665772	-1.512797	-1.078504	-0.330589	-0.426230	1.191438	0.0	0.241988	1.506840	0.931603	-1.077862	2.447870	0.764998	-0.368715	1.647603
1156	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	0	1	0	0	0.336849	0.202082	0.716610	0.085049	0.0	1.002185	-1.575686	0.694247	-1.026167	0.845911	0.246200	0.835481	1.614379	-0.678049	-0.603911	-0.426230	1.191438	0.0	1.415991	0.864013	-0.620189	0.338096	1.794749	2.973780	3.666880	2.208310
1177	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	0	0	1	1.431867	0.026066	0.963398	2.038524	0.0	1.048711	1.169781	-0.781948	-1.026167	0.845911	-1.567907	1.437645	1.077653	0.923771	-0.057267	-0.426230	0.266233	0.0	2.589994	0.992578	0.155707	0.338096	1.141629	1.869389	-0.368715	1.927956
1181	0	0	1	0	1	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.322365	-0.836662	-0.393938	-1.868426	0.0	1.057019	0.254625	-1.224807	-1.026167	1.749610	0.246200	1.585743	-0.373994	-0.277594	1.036019	-0.426230	-0.658973	0.0	0.241988	2.406799	0.155707	0.338096	1.304909	1.869389	-0.058285	2.208310
1184	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.869874	-0.541648	1.580370	2.038524	0.0	1.063666	-0.660531	1.235519	0.379672	1.749610	0.246200	2.320919	0.614430	0.122861	2.675949	2.346151	0.266233	0.0	0.241988	3.178192	2.483396	0.338096	0.488508	1.041095	0.562576	0.806541
1185	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	0.665354	1.211077	0.716610	-0.891688	0.0	1.065327	0.254625	-0.043851	-1.026167	1.749610	0.246200	2.358528	-1.516170	-0.678049	2.402628	2.346151	-1.584178	0.0	0.241988	0.349751	0.155707	0.338096	1.141629	1.593291	1.183437	1.927956
1221	1	0	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.884358	1.079684	-1.010909	-1.868426	0.0	1.141762	0.254625	0.399008	-1.026167	0.845911	0.246200	0.901986	-1.534440	0.122861	2.129306	2.346151	1.191438	0.0	0.241988	1.763972	0.155707	-1.077862	2.611150	2.973780	3.666880	-0.034520
1223	0	1	0	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.103362	0.720215	-0.023755	0.085049	0.0	1.148409	0.254625	0.792660	-2.432006	1.749610	0.246200	1.366890	1.384454	1.724681	-1.150554	-0.426230	0.266233	0.0	-0.932014	1.763972	0.155707	-2.493820	2.611150	0.212802	3.666880	1.647603
1225	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	0.884358	0.502054	2.320735	-0.891688	0.0	1.153393	1.169781	-0.880361	-1.026167	1.749610	-0.660853	2.167509	0.394343	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	1.249709	-0.620189	0.338096	2.284590	0.488900	1.804297	0.526188
1242	0	0	1	0	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	1	0	0	0.336849	-0.474713	-0.270544	1.061787	0.0	1.188288	-0.660531	1.087899	0.379672	2.653309	-0.660853	2.832355	-1.400364	-0.678049	-0.330589	-0.426230	-0.658973	0.0	-0.932014	1.249709	0.155707	-1.077862	2.284590	1.041095	3.046019	1.086895
1295	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	0	1	0	0	1	0.446350	-0.016079	-0.640727	-1.868426	0.0	1.312910	0.254625	0.743454	0.379672	0.845911	0.246200	0.838030	1.706855	-1.078504	-0.603911	-0.426230	1.191438	0.0	0.241988	1.506840	0.155707	1.754054	2.447870	2.697683	3.356449	0.245834
1301	1	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	2.307882	-1.121758	-0.887515	0.085049	0.0	1.327864	-0.660531	-0.683535	0.379672	1.749610	-0.660853	2.079756	1.161416	0.523316	1.855984	2.346151	1.191438	0.0	0.241988	3.306758	-2.171982	-1.077862	1.468189	1.317193	3.666880	2.769018
1303	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	1	0	1	0	0	1	1.103362	0.492137	-0.640727	0.085049	0.0	1.332849	0.254625	1.284725	-1.026167	0.845911	-0.660853	0.813808	0.696787	2.125136	-0.877232	-0.426230	0.266233	0.0	0.241988	2.149668	0.931603	0.338096	2.447870	1.869389	3.666880	1.647603
1327	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	1	0.993860	1.280492	-0.764121	0.085049	0.0	1.392668	-1.575686	-1.027981	1.785511	1.749610	-1.567907	1.428296	-0.923931	-0.277594	-0.877232	-0.426230	1.191438	0.0	0.241988	1.763972	1.707500	0.338096	1.958030	3.525976	-0.058285	1.086895
1330	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.665354	0.050857	-0.393938	0.085049	0.0	1.397653	-1.575686	0.743454	-1.026167	2.653309	0.246200	2.738652	1.156075	1.724681	-0.603911	-0.426230	1.191438	0.0	-0.932014	1.249709	-0.620189	0.338096	1.468189	2.145487	1.183437	2.769018
1331	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.212863	1.044977	0.099639	0.085049	0.0	1.399314	1.169781	1.235519	-1.026167	2.653309	-0.660853	2.796659	-0.102609	0.523316	-0.877232	-0.426230	1.191438	0.0	-0.932014	2.278234	0.155707	0.338096	2.447870	1.593291	3.046019	1.367249
1348	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	1	0	0	0.336849	0.829295	-1.010909	1.061787	0.0	1.440855	-1.575686	1.579964	0.379672	1.749610	-1.567907	2.192794	0.657436	-0.277594	-1.150554	-0.426230	-1.584178	0.0	0.241988	1.378275	0.155707	0.338096	1.958030	0.764998	2.735589	3.329725
1351	0	1	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.212863	-1.699389	1.580370	0.085049	0.0	1.454148	1.169781	-0.388296	0.379672	1.749610	1.153254	2.267374	-1.669079	0.122861	-1.150554	-0.426230	-0.658973	0.0	0.241988	1.635406	0.155707	0.338096	2.447870	3.525976	0.562576	0.806541
1403	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	1	0	0	0.227347	-1.694431	0.716610	1.061787	0.0	1.578770	-0.660531	0.546627	0.379672	1.749610	-1.567907	1.452944	1.515719	-1.078504	-0.877232	-0.426230	-1.584178	0.0	-0.932014	1.249709	0.155707	0.338096	2.121310	1.041095	2.735589	1.647603
1414	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.103362	0.935897	1.950552	0.085049	0.0	1.608679	-1.575686	0.891073	0.379672	0.845911	0.246200	0.452594	-0.172739	-0.277594	2.129306	2.346151	-0.658973	0.0	-0.932014	1.763972	0.155707	0.338096	1.631469	2.697683	3.046019	1.927956
1430	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	1	0	0	0.117845	-1.491145	0.099639	0.085049	0.0	1.645235	-0.660531	1.629171	-2.432006	0.845911	0.246200	1.424259	-1.537111	0.122861	-0.877232	-0.426230	-1.584178	0.0	0.241988	1.121144	0.155707	0.338096	1.794749	3.249878	-0.368715	1.927956
1443	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.555852	-1.245713	-0.887515	0.085049	0.0	1.671821	-1.575686	-0.486709	0.379672	2.653309	0.246200	2.629863	0.421468	0.923771	-1.150554	-0.426230	-1.584178	0.0	-0.932014	1.635406	-0.620189	-1.077862	2.447870	0.488900	0.562576	2.769018
1445	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	0	0	0	1	0.446350	-0.546607	2.320735	1.061787	0.0	1.676806	-1.575686	-0.289883	-1.026167	1.749610	-0.660853	1.501601	-1.218926	-1.078504	2.129306	2.346151	0.266233	0.0	0.241988	1.249709	0.155707	0.338096	2.121310	0.764998	-0.679146	1.647603
1462	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	1	0	0	0.227347	-0.199532	1.827158	-1.868426	0.0	1.713362	-0.660531	-0.289883	-1.026167	1.749610	1.153254	1.174597	-0.770882	-1.078504	-1.150554	-0.426230	-1.584178	0.0	0.241988	1.249709	-0.620189	-1.077862	2.121310	1.317193	2.114728	0.526188

index_3 = np.where(y_means==3)
index_3

(array([  29,   50,   65,   67,   70,   77,   82,   85,   95,  105,  106,
         112,  131,  136,  147,  163,  165,  189,  209,  213,  233,  279,
         280,  290,  316,  329,  348,  367,  375,  376,  379,  392,  401,
         406,  408,  424,  429,  455,  465,  489,  492,  497,  510,  534,
         536,  541,  552,  568,  575,  588,  609,  624,  625,  627,  646,
         674,  706,  714,  721,  723,  728,  741,  743,  745,  755,  758,
         766,  770,  771,  774,  779,  787,  789,  804,  806,  812,  821,
         851,  867,  869,  887,  890,  898,  899,  904,  916,  919,  936,
         945,  947,  956,  966,  971,  987,  994, 1010, 1031, 1034, 1054,
        1080, 1103, 1126, 1129, 1154, 1166, 1176, 1194, 1195, 1209, 1231,
        1235, 1264, 1268, 1275, 1277, 1278, 1305, 1310, 1357, 1370, 1374,
        1377, 1396, 1401, 1437, 1461], dtype=int64),)

Group_3 = x.iloc[index_3]
Group_3

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
29	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0.993860	-0.241677	-0.887515	1.061787	0.0	-1.639803	-0.660531	0.841867	0.379672	2.653309	-1.567907	2.644099	1.195848	0.122861	-0.877232	-0.426230	1.191438	0.0	-0.932014	1.378275	-0.620189	-1.077862	-0.817734	-0.615492	-0.058285	-0.875581
50	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	0	0	1	1.212863	-0.437526	-1.010909	-0.891688	0.0	-1.596600	-1.575686	1.579964	-1.026167	0.845911	0.246200	-0.238386	0.700020	2.525591	-0.603911	-0.426230	1.191438	0.0	-0.932014	1.506840	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935
65	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	0	1	0	0	1	1.979376	0.083085	-0.147150	0.085049	0.0	-1.563368	1.169781	-1.618459	0.379672	1.749610	0.246200	1.753601	0.761296	-0.277594	-0.330589	-0.426230	0.266233	0.0	2.589994	1.249709	-0.620189	0.338096	-0.327893	-1.167687	-0.679146	-0.595227
67	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	1	1	0	0	0	1	0.884358	1.330074	-0.270544	0.085049	0.0	-1.560045	-0.660531	-0.339090	0.379672	0.845911	-1.567907	0.684408	0.628766	-0.277594	0.489376	-0.426230	0.266233	0.0	0.241988	1.763972	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935
70	0	1	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	1	2.417384	1.047456	-1.010909	-1.868426	0.0	-1.551736	-1.575686	-0.437503	-1.026167	-0.057788	0.246200	-0.218838	1.455287	1.724681	-1.150554	-0.426230	1.191438	0.0	-0.932014	1.121144	-0.620189	-1.077862	-0.491174	-0.339394	-0.368715	-0.314873
77	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	1	0.884358	-1.510977	-0.393938	1.061787	0.0	-1.535120	1.169781	-0.683535	0.379672	0.845911	-1.567907	1.432546	0.105953	0.523316	-0.330589	-0.426230	-0.658973	0.0	-0.932014	0.735447	0.155707	1.754054	-1.144294	-1.167687	-0.679146	-1.155935
82	0	0	1	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	0	0	1	1.979376	-1.714264	-1.010909	-0.891688	0.0	-1.526812	-1.575686	0.202182	0.379672	0.845911	1.153254	0.793835	0.531090	0.122861	-0.330589	-0.426230	1.191438	0.0	0.241988	1.635406	0.931603	0.338096	-0.981014	-1.167687	-0.368715	-1.155935
85	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	2.088878	1.481299	-0.270544	0.085049	0.0	-1.516842	1.169781	-0.831155	-2.432006	0.845911	1.153254	0.160861	1.037880	0.523316	-1.150554	-0.426230	-1.584178	0.0	-0.932014	3.306758	0.155707	-1.077862	-0.164613	-0.063296	-0.679146	-0.595227
95	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.869874	1.027623	-0.887515	1.061787	0.0	-1.493579	-1.575686	-0.289883	0.379672	0.845911	0.246200	1.497139	1.361546	2.525591	-0.877232	-0.426230	-1.584178	0.0	0.241988	0.606882	1.707500	-2.493820	-0.491174	-0.339394	-0.679146	-0.314873
105	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	2.417384	1.530881	-0.887515	1.061787	0.0	-1.470317	0.254625	-1.667666	-1.026167	2.653309	1.153254	2.622214	1.069361	2.525591	1.582663	2.346151	1.191438	0.0	0.241988	2.406799	0.155707	0.338096	-0.654454	-0.615492	-0.058285	-0.595227
106	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	1	1.431867	0.774755	-1.010909	0.085049	0.0	-1.468655	-1.575686	0.349801	0.379672	2.653309	-0.660853	2.479428	-0.640600	0.122861	1.036019	-0.426230	-1.584178	0.0	-0.932014	2.149668	-1.396086	-1.077862	0.161947	-0.339394	-0.679146	0.806541
112	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	1	0	0	1	1.869874	-1.637412	2.073946	0.085049	0.0	-1.457024	1.169781	-1.766079	1.785511	1.749610	1.153254	2.300096	-0.062134	-0.277594	-0.877232	-0.426230	0.266233	0.0	-0.932014	1.506840	0.155707	0.338096	-0.327893	-0.339394	0.562576	-0.034520
131	0	1	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	1	1.431867	0.016150	0.346427	0.085049	0.0	-1.413821	0.254625	0.546627	0.379672	0.845911	1.153254	0.574769	-1.077682	0.523316	-1.150554	-0.426230	1.191438	0.0	-0.932014	0.606882	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.875581
136	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	1.541369	0.861523	-0.147150	1.061787	0.0	-1.405513	-1.575686	-0.634329	-2.432006	0.845911	1.153254	0.881164	1.523028	-0.277594	-0.057267	-0.426230	1.191438	0.0	-0.932014	0.864013	-0.620189	0.338096	-0.491174	-0.615492	-0.679146	-0.314873
147	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	0.446350	0.135146	0.099639	0.085049	0.0	-1.372281	1.169781	1.235519	-1.026167	1.749610	-1.567907	2.268862	-0.200285	0.523316	-0.603911	-0.426230	-0.658973	0.0	0.241988	1.249709	-0.620189	-1.077862	-0.001333	0.488900	1.493867	0.806541
163	0	0	1	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	1	2.198380	-1.161424	1.827158	-0.891688	0.0	-1.332402	0.254625	0.841867	1.785511	0.845911	1.153254	0.623852	1.277362	0.122861	0.216054	-0.426230	-0.658973	0.0	0.241988	0.092620	-0.620189	-2.493820	-0.327893	-0.339394	-0.368715	-0.034520
165	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.431867	1.610212	0.223033	0.085049	0.0	-1.327417	0.254625	-0.634329	0.379672	2.653309	-0.660853	2.852116	0.385068	0.122861	-0.057267	-0.426230	-0.658973	0.0	-0.932014	1.249709	1.707500	0.338096	-0.327893	-0.063296	0.562576	-0.034520
189	0	0	1	0	1	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.541369	-1.213485	-0.764121	0.085049	0.0	-1.274245	1.169781	1.579964	0.379672	1.749610	-0.660853	1.536448	-1.000807	0.122861	0.762698	-0.426230	0.266233	0.0	-0.932014	1.249709	2.483396	0.338096	-0.001333	0.764998	-0.368715	-1.155935
209	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	1	0.993860	-0.392902	-1.010909	1.061787	0.0	-1.224396	1.169781	1.530758	0.379672	0.845911	-1.567907	0.614715	-0.273367	1.724681	0.216054	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.491174	-0.615492	-0.679146	-0.314873
213	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.541369	1.652357	-0.147150	1.061787	0.0	-1.211103	-0.660531	0.743454	-1.026167	0.845911	-0.660853	1.272124	0.199975	0.923771	0.216054	-0.426230	1.191438	0.0	1.415991	0.606882	1.707500	-2.493820	0.488508	1.317193	0.562576	0.806541
233	0	0	1	0	0	1	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.431867	0.127709	-1.010909	1.061787	0.0	-1.166239	1.169781	0.103769	0.379672	2.653309	1.153254	2.765212	1.377989	0.122861	-1.150554	-0.426230	0.266233	0.0	0.241988	2.663930	0.155707	-1.077862	-0.001333	-1.167687	-0.679146	0.526188
279	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	0	0	1	1.431867	-0.013600	-0.640727	-1.868426	0.0	-1.063218	-1.575686	1.481551	0.379672	2.653309	-0.660853	2.685957	0.211078	0.122861	-0.330589	-0.426230	-1.584178	0.0	1.415991	2.149668	0.931603	-1.077862	0.488508	-0.063296	-0.368715	0.526188
280	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	1	1.760373	0.663195	-0.764121	1.061787	0.0	-1.061556	0.254625	-1.027981	0.379672	1.749610	0.246200	2.354491	0.942031	0.122861	0.216054	-0.426230	1.191438	0.0	2.589994	1.249709	1.707500	-1.077862	-0.327893	-0.339394	-0.368715	-0.314873
290	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	1	0	0	1	1.322365	-0.412735	0.099639	1.061787	0.0	-1.044940	0.254625	-1.520046	0.379672	2.653309	-1.567907	2.584180	1.585428	2.525591	-1.150554	-0.426230	1.191438	0.0	-0.932014	1.378275	0.931603	0.338096	-0.654454	-0.615492	-0.368715	-0.595227
316	0	0	1	0	1	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	1	1.322365	0.715256	-1.010909	-0.891688	0.0	-0.986783	0.254625	1.186312	-1.026167	1.749610	0.246200	1.585318	0.491457	1.724681	-0.877232	-0.426230	1.191438	0.0	-0.932014	1.763972	-0.620189	0.338096	-0.001333	-0.891589	-0.679146	0.806541
329	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.103362	1.684585	-0.517332	2.038524	0.0	-0.960197	1.169781	-1.175601	0.379672	2.653309	0.246200	2.506625	0.289781	0.523316	-1.150554	-0.426230	-0.658973	0.0	0.241988	1.249709	-0.620189	0.338096	-0.654454	-0.615492	-0.368715	-0.875581
348	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	1	0.555852	0.018629	1.703764	2.038524	0.0	-0.925303	-1.575686	-1.077188	0.379672	1.749610	1.153254	2.016225	0.223164	-0.277594	-0.330589	-0.426230	-0.658973	0.0	-0.932014	0.606882	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935
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1010	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	0	0	1	1.979376	0.826816	-1.010909	1.061787	0.0	0.663213	-0.660531	0.743454	1.785511	1.749610	1.153254	1.748501	-0.266902	-0.277594	-0.603911	-0.426230	1.191438	0.0	1.415991	2.535365	0.931603	1.754054	-0.001333	0.764998	-0.679146	-1.155935
1031	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	1	0.993860	-1.054823	-0.023755	0.085049	0.0	0.718047	-1.575686	-0.683535	0.379672	0.845911	1.153254	0.763450	0.235110	0.523316	-1.150554	-0.426230	-1.584178	0.0	0.241988	2.149668	-1.396086	1.754054	-0.001333	0.764998	0.562576	-0.314873
1034	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	1	0.884358	0.583864	1.333581	0.085049	0.0	0.723032	-0.660531	1.432345	-2.432006	0.845911	-1.567907	0.923872	0.779988	-0.277594	0.762698	-0.426230	-0.658973	0.0	0.241988	1.635406	-0.620189	0.338096	-0.001333	0.764998	-0.679146	0.806541
1054	0	0	1	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.322365	1.704418	-0.270544	1.061787	0.0	0.762911	0.254625	-1.520046	0.379672	0.845911	-0.660853	0.842067	0.932475	0.122861	-0.330589	-0.426230	-0.658973	0.0	1.415991	2.278234	0.155707	0.338096	0.161947	0.764998	-0.679146	0.806541
1080	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0.993860	-1.424209	-0.764121	0.085049	0.0	0.834361	0.254625	-0.732742	0.379672	1.749610	-0.660853	2.146686	-0.412221	2.125136	-0.877232	-0.426230	1.191438	0.0	0.241988	1.506840	-0.620189	1.754054	0.978348	2.145487	0.873006	-0.875581
1103	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	1	1.212863	-0.769726	0.840004	1.061787	0.0	0.884210	0.254625	1.481551	0.379672	-0.057788	0.246200	-0.013584	-0.087150	2.125136	-0.330589	-0.426230	0.266233	0.0	0.241988	0.864013	-0.620189	0.338096	0.161947	0.764998	1.493867	0.806541
1126	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.431867	-1.334961	-0.023755	0.085049	0.0	0.940705	0.254625	-0.339090	0.379672	2.653309	0.246200	2.725691	0.731642	0.523316	0.216054	-0.426230	0.266233	0.0	0.241988	2.021103	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935
1129	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0.555852	0.635925	-0.023755	-0.891688	0.0	0.947352	1.169781	1.333932	-1.026167	2.653309	1.153254	2.785610	1.693363	2.125136	1.855984	2.346151	1.191438	0.0	-0.932014	1.635406	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-0.875581
1154	0	0	1	1	0	0	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.103362	0.925980	2.073946	1.061787	0.0	0.997200	1.169781	1.579964	0.379672	2.653309	0.246200	2.795171	-1.277953	0.122861	-1.150554	-0.426230	0.266233	0.0	0.241988	2.021103	-0.620189	0.338096	-0.327893	-0.615492	-0.368715	-1.155935
1166	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.212863	-1.084572	-0.640727	2.038524	0.0	1.028771	0.254625	1.137106	-1.026167	1.749610	1.153254	1.848366	-1.224267	-0.277594	2.675949	2.346151	-0.658973	0.0	0.241988	1.506840	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
1176	0	0	1	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.322365	-1.243234	1.580370	1.061787	0.0	1.047049	-1.575686	0.300595	0.379672	1.749610	-0.660853	2.105678	-1.519965	0.122861	0.216054	-0.426230	-0.658973	0.0	1.415991	2.021103	-0.620189	0.338096	-0.491174	-0.615492	-0.368715	-0.595227
1194	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	0	1	1	0	0	0	1	1.103362	1.047456	-0.887515	1.061787	0.0	1.081943	-0.660531	-0.929568	1.785511	1.749610	-0.660853	2.011975	0.951869	1.324226	-0.330589	-0.426230	0.266233	0.0	2.589994	2.278234	-0.620189	0.338096	-0.654454	-0.615492	-0.368715	-0.595227
1195	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1.322365	0.016150	-1.010909	0.085049	0.0	1.083605	0.254625	-1.470840	0.379672	1.749610	0.246200	1.885975	1.134291	0.523316	-0.330589	-0.426230	-1.584178	0.0	-0.932014	1.506840	-0.620189	0.338096	0.161947	0.764998	-0.679146	-1.155935
1209	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	1	2.526886	-1.072176	-1.010909	1.061787	0.0	1.116838	0.254625	1.284725	-2.432006	0.845911	1.153254	0.930671	0.864874	0.122861	1.309341	2.346151	0.266233	0.0	0.241988	0.992578	-0.620189	1.754054	-0.981014	-1.167687	-0.679146	-1.155935
1231	0	0	1	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0.993860	-0.211928	0.469821	1.061787	0.0	1.166686	0.254625	-1.569253	0.379672	-0.057788	-0.660853	-0.199928	-0.648751	1.324226	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.992578	0.155707	0.338096	0.488508	0.764998	-0.679146	1.367249
1235	0	0	1	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	0	1	0	0	1	0.993860	1.176369	-0.887515	0.085049	0.0	1.174995	0.254625	0.399008	0.379672	0.845911	1.153254	0.821245	-1.225110	0.523316	-0.877232	-0.426230	-0.658973	0.0	0.241988	0.221185	1.707500	-1.077862	0.488508	0.488900	-0.679146	-0.314873
1264	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.979376	-0.804433	-0.887515	0.085049	0.0	1.238136	0.254625	-0.289883	-1.026167	2.653309	-1.567907	2.663434	0.771836	2.125136	-0.877232	-0.426230	-0.658973	0.0	2.589994	2.921061	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935
1268	1	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	0	0	1	1.760373	-0.350758	-1.010909	1.061787	0.0	1.246445	-1.575686	-0.289883	-1.026167	1.749610	0.246200	1.373052	1.123610	0.523316	1.309341	2.346151	1.191438	0.0	2.589994	2.021103	-0.620189	-1.077862	-0.654454	-0.615492	-0.679146	-0.595227
1275	0	0	1	0	1	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	1.541369	0.345870	-0.764121	0.085049	0.0	1.264722	-1.575686	-0.634329	0.379672	0.845911	0.246200	1.405136	1.218616	-0.277594	-1.150554	-0.426230	1.191438	0.0	-0.932014	0.478316	-0.620189	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
1277	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	1	0	1	0	0	1	0.993860	-0.169783	-0.887515	1.061787	0.0	1.269707	0.254625	-0.978775	0.379672	2.653309	1.153254	2.725053	-0.013366	1.724681	0.489376	-0.426230	0.266233	0.0	0.241988	1.635406	0.155707	0.338096	-0.817734	-0.891589	-0.058285	-0.595227
1278	0	0	1	0	1	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	0	1	1	0	0	-0.101159	1.439154	0.099639	0.085049	0.0	1.271369	1.169781	1.186312	0.379672	0.845911	-1.567907	0.386301	1.635320	1.724681	-0.603911	-0.426230	1.191438	0.0	0.241988	0.478316	-1.396086	0.338096	0.815068	1.041095	0.873006	0.806541
1305	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.869874	-0.920951	-0.270544	1.061787	0.0	1.337834	1.169781	0.103769	0.379672	-0.057788	1.153254	0.074595	0.194354	0.523316	-0.057267	-0.426230	-0.658973	0.0	0.241988	0.349751	-0.620189	-1.077862	-0.001333	-0.891589	-0.368715	0.806541
1310	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	0	1	0	0	1	2.307882	1.025144	0.716610	1.061787	0.0	1.349466	-1.575686	1.038693	0.379672	1.749610	0.246200	1.972666	1.027480	-0.277594	-0.330589	-0.426230	-0.658973	0.0	-0.932014	1.506840	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
1357	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	0.555852	1.471383	-0.393938	0.085049	0.0	1.472426	0.254625	0.841867	0.379672	0.845911	-1.567907	1.454431	0.074332	2.525591	-0.603911	-0.426230	-0.658973	0.0	0.241988	0.864013	0.155707	1.754054	0.978348	0.764998	0.873006	0.806541
1370	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	1.103362	0.908626	0.593216	1.061787	0.0	1.510643	0.254625	-0.093057	0.379672	-0.057788	-0.660853	-0.220113	-1.712927	2.125136	0.762698	-0.426230	0.266233	0.0	0.241988	0.606882	0.931603	1.754054	0.161947	0.764998	-0.368715	0.806541
1374	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	2.307882	-0.489587	1.456975	0.085049	0.0	1.517290	1.169781	0.300595	0.379672	1.749610	1.153254	2.416322	-0.358675	0.523316	-0.603911	-0.426230	0.266233	0.0	0.241988	2.278234	-0.620189	-1.077862	-0.981014	-1.167687	-0.679146	-1.155935
1377	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	0	0	0	1	1.322365	0.648321	-0.887515	-1.868426	0.0	1.522275	-0.660531	-1.175601	0.379672	2.653309	1.153254	2.689569	-0.080826	0.122861	-0.057267	-0.426230	1.191438	0.0	-0.932014	2.149668	0.155707	0.338096	-0.327893	-0.063296	0.562576	-0.314873
1396	0	0	1	0	0	1	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	0	0	1	1.760373	0.906147	1.827158	1.061787	0.0	1.567139	-1.575686	0.005356	0.379672	0.845911	-1.567907	0.838243	-1.190396	1.324226	-0.603911	-0.426230	-0.658973	0.0	-0.932014	0.478316	-0.620189	-1.077862	-0.817734	-0.615492	-0.058285	-0.595227
1401	0	0	1	1	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1	0	0	1	1.979376	-1.520894	2.073946	1.061787	0.0	1.575447	0.254625	0.251388	1.785511	2.653309	-0.660853	2.790497	1.615925	0.523316	0.762698	-0.426230	-1.584178	0.0	0.241988	3.049627	-2.171982	0.338096	0.488508	1.317193	-0.368715	-0.034520
1437	1	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	1	0	0	0.227347	-1.729138	-0.023755	0.085049	0.0	1.656867	1.169781	1.038693	0.379672	2.653309	1.153254	2.746939	0.138980	-0.277594	-0.603911	-0.426230	0.266233	0.0	-0.932014	1.249709	0.155707	-1.077862	-0.164613	-1.167687	-0.368715	-0.314873
1461	0	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	0	1	0	0	1	1.431867	-0.973012	2.320735	0.085049	0.0	1.711700	1.169781	-1.323220	-1.026167	0.845911	-1.567907	0.924509	0.319435	0.523316	-0.603911	-0.426230	-0.658973	0.0	0.241988	1.121144	0.155707	0.338096	-0.654454	-0.615492	-0.058285	-1.155935

index_4 = np.where(y_means==4)
index_4

(array([   3,    7,   10,   12,   13,   14,   16,   17,   20,   21,   23,
          24,   30,   32,   34,   37,   39,   40,   41,   42,   47,   49,
          51,   57,   68,   69,   71,   72,   74,   81,   84,   86,   96,
          97,  100,  101,  102,  107,  108,  109,  113,  118,  125,  127,
         128,  138,  140,  143,  145,  146,  148,  149,  160,  161,  162,
         164,  169,  170,  171,  176,  177,  179,  180,  181,  182,  191,
         200,  202,  206,  207,  214,  217,  221,  224,  229,  238,  239,
         241,  246,  252,  255,  259,  260,  262,  264,  267,  272,  274,
         278,  284,  288,  289,  292,  294,  296,  301,  302,  309,  312,
         318,  320,  323,  328,  331,  335,  336,  337,  345,  349,  350,
         351,  357,  362,  363,  369,  370,  373,  377,  380,  381,  382,
         383,  385,  397,  399,  402,  404,  405,  407,  410,  414,  415,
         416,  418,  419,  421,  422,  430,  436,  437,  443,  449,  453,
         457,  460,  461,  463,  470,  474,  475,  476,  478,  479,  480,
         481,  485,  487,  490,  494,  495,  496,  498,  501,  505,  507,
         512,  513,  515,  516,  517,  521,  522,  546,  550,  553,  555,
         563,  565,  571,  574,  576,  577,  579,  580,  581,  585,  586,
         589,  598,  602,  606,  613,  614,  615,  618,  620,  629,  630,
         631,  633,  634,  637,  638,  640,  642,  644,  645,  655,  656,
         657,  659,  662,  663,  666,  668,  670,  671,  673,  680,  682,
         683,  688,  689,  694,  697,  698,  709,  711,  712,  720,  722,
         724,  725,  726,  727,  731,  732,  733,  734,  739,  740,  754,
         762,  763,  764,  765,  769,  776,  777,  781,  786,  793,  794,
         797,  798,  800,  802,  803,  808,  815,  818,  819,  820,  823,
         826,  827,  828,  831,  833,  834,  835,  841,  842,  848,  850,
         853,  856,  859,  860,  863,  868,  871,  876,  880,  885,  892,
         893,  902,  906,  909,  910,  911,  912,  915,  917,  921,  924,
         929,  933,  934,  939,  940,  952,  965,  967,  972,  974,  977,
         980,  981,  982,  984,  991,  993,  996,  998, 1001, 1003, 1004,
        1012, 1016, 1017, 1021, 1022, 1025, 1027, 1032, 1039, 1041, 1045,
        1047, 1052, 1056, 1059, 1060, 1061, 1064, 1068, 1069, 1070, 1072,
        1082, 1091, 1097, 1100, 1102, 1104, 1108, 1110, 1115, 1118, 1125,
        1127, 1132, 1134, 1136, 1137, 1139, 1141, 1149, 1151, 1152, 1153,
        1168, 1169, 1170, 1175, 1178, 1180, 1182, 1189, 1193, 1197, 1198,
        1201, 1205, 1213, 1215, 1217, 1222, 1224, 1227, 1230, 1233, 1238,
        1239, 1245, 1247, 1248, 1249, 1252, 1256, 1258, 1266, 1271, 1272,
        1273, 1276, 1283, 1297, 1302, 1307, 1309, 1311, 1312, 1313, 1317,
        1320, 1323, 1329, 1332, 1337, 1338, 1339, 1343, 1345, 1349, 1353,
        1365, 1367, 1375, 1376, 1379, 1381, 1382, 1383, 1387, 1390, 1400,
        1402, 1407, 1408, 1411, 1413, 1415, 1417, 1420, 1423, 1426, 1428,
        1433, 1436, 1438, 1449, 1458, 1460, 1464, 1467, 1469], dtype=int64),)

Group_4 = x.iloc[index_4]
Group_4

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Age Group_Adult	Age Group_Child	Age Group_Old	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	1	0	0	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	1.243211	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
7	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	1	1	0	1	0	0	-0.758170	1.377177	1.827158	-1.868426	0.0	-1.684667	1.169781	0.054562	0.379672	-0.961486	0.246200	-0.809529	-0.137464	-0.678049	1.855984	2.346151	-0.658973	0.0	0.241988	-1.321601	-0.620189	0.338096	-0.981014	-1.167687	-0.679146	-1.155935
10	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.210661	0.016150	0.840004	0.085049	0.0	-1.679682	-1.575686	0.891073	1.785511	-0.961486	-0.660853	-0.866261	0.304397	-1.078504	-0.603911	-0.426230	0.266233	0.0	0.241988	-0.678774	1.707500	0.338096	-0.327893	-0.063296	-0.679146	-0.314873
12	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	1	0	0	1	1	0	1	0	0	-0.648668	-0.328446	2.073946	-1.868426	0.0	-1.676359	-1.575686	-1.716872	0.379672	-0.961486	0.246200	-0.763209	0.120429	-0.678049	0.489376	-0.426230	1.191438	0.0	0.241988	-0.807339	-1.396086	-1.077862	-0.327893	-0.615492	0.562576	-0.314873
13	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	1	0	0	1	1	0	1	0	0	-0.320163	1.347427	1.210187	-0.891688	0.0	-1.673035	-0.660531	1.333932	0.379672	-0.961486	1.153254	-0.816328	-0.780719	-1.078504	-1.150554	-0.426230	0.266233	0.0	0.241988	-1.064470	-0.620189	0.338096	-0.817734	-0.615492	-0.368715	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1458	0	0	1	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	1	0	0	-0.210661	-1.277942	-1.010909	1.061787	0.0	1.706715	0.254625	-0.191470	-2.432006	-0.961486	1.153254	-0.749185	-0.753454	-0.678049	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.935905	1.707500	0.338096	-0.491174	-0.339394	-0.368715	-0.875581
1460	0	0	1	0	1	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	1	1	1	0	1	0	0	-0.867672	-0.829224	2.320735	1.061787	0.0	1.710039	1.169781	0.349801	-1.026167	-0.961486	-1.567907	-0.577502	-0.818525	-0.678049	-0.330589	-0.426230	-0.658973	0.0	-0.932014	-0.807339	0.155707	-2.493820	-0.327893	-0.063296	-0.679146	-0.034520
1464	0	0	1	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	1	0	0	1	1	1	0	1	0	0	-1.196177	0.903668	-0.517332	0.085049	0.0	1.720008	1.169781	-1.766079	-1.026167	-0.961486	0.246200	-0.751522	0.992907	-1.078504	0.762698	-0.426230	1.191438	0.0	-0.932014	-0.807339	-0.620189	0.338096	-0.491174	-0.615492	-0.679146	-1.155935
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-1.284418	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	1	0	0	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.574124	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227

449 rows × 58 columns

plt.scatter(X_principal.iloc[y_means==0,0],X_principal.iloc[y_means==0,1],s=25,c='yellow',label="Group_0")
plt.scatter(X_principal.iloc[y_means==1,0],X_principal.iloc[y_means==1,1],s=25,c='green',label="Group_1")
plt.scatter(X_principal.iloc[y_means==2,0],X_principal.iloc[y_means==2,1],s=25,c='blue',label="Group_2")
plt.scatter(X_principal.iloc[y_means==3,0],X_principal.iloc[y_means==3,1],s=25,c='purple',label="Group_3")
plt.scatter(X_principal.iloc[y_means==4,0],X_principal.iloc[y_means==4,1],s=25,c='black',label="Group_4")
plt.legend()
plt.show()

silhouette_score(X_principal,y_means)

0.4255702350140425

Heirarchical clustering

plt.figure(figsize =(8, 8))
plt.title('Visualising the data')
Dendrogram = shc.dendrogram((shc.linkage(X_principal, method ='ward')))

ac2 = AgglomerativeClustering(n_clusters = 3)
 
# Visualizing the clustering
plt.figure(figsize =(6, 6))
plt.scatter(X_principal['P1'], X_principal['P2'],
           c = ac2.fit_predict(X_principal))
plt.show()

cluster = AgglomerativeClustering(n_clusters=2,affinity = 'euclidean', linkage = 'ward')

HC = cluster.fit_predict(X_principal)

HC

array([1, 0, 1, ..., 1, 0, 1], dtype=int64)

silhouette_score(X_principal,HC)

0.3999025735742288

DBSCAN Clustering

from sklearn.cluster import DBSCAN

DBC = DBSCAN(eps=0.8,min_samples=15)
Label = DBC.fit_predict(X_principal)

np.unique(Label)

array([-1,  0], dtype=int64)

silhouette_score(X_principal,Label)

0.4745192062196644

plt.scatter(X_principal['P1'],X_principal['P2'],c= Label,s=60)
plt.title('DBSCAN Clusters')
plt.show()